Why I Chose My Major free essay sample

The first time I realized that I wanted to be an engineer was during my sophomore year of high school while taking a CAD class. I was working on a project where we needed to improve an existing product. Doing this project helped me realize how satisfying it was to research a product, find a creative way to make it better, and model your idea into a prototype. My choice to pursue engineering was made clear during my junior year in my PLTW and physics classes. PLTW confirmed my interest in engineering through the many projects we did throughout the year, and physics helped me discover my interest in electronics. Currently, during my senior year, I am taking a digital electronics course, which is already one of my favorite classes I have taken throughout all of high school. Apart from my math and science based classes, my experience with the band program during high school has also helped me realize that engineering is for me. We will write a custom essay sample on Why I Chose My Major or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page In band, especially marching band, a tremendous amount of teamwork is required to sound good. As a member of the drum line, everyone needs to work together and practice extensively so we can all sound like one person. Also, the drum line needs to be able to work well with the band so everyone can blend well to play as well as we can. This teamwork is very similar to what is required as an engineer. Everyone needs to be able to contribute and listen to others’ ideas in order for the best solution to come out of their project. High school has helped me realize who I am and what I want to become, and college will help me become that person.

Internet, Facebook, Google Talk, email, MS word Essays

Internet, Facebook, Google Talk, email, MS word Essays Internet, Facebook, Google Talk, email, MS word Essay Internet, Facebook, Google Talk, email, MS word Essay Essay Topic: Talk The Good Morrow by John Donne-a Flawless Metaphysical Poem The Good Morrow is a typical Donnian love poem, divided into three stanzas. Its one of those love poems in which he praises the spiritual relationship between men and women and hails it so ardently. In the opening stanza, the poet expresses his wonder as to what he and his beloved did before they fell in love with each other. He becomes surprised remembering their past love experiences. He compares the love experiences of himself and his beloved with weaning, falsely sucking country pleasures and snorting. The reference to hese three physical activities indicates that they spent a life of worldly enjoyment. But now the poet using the conjunction But makes a contrast and says that all these past physical activities seem to be utterly meaningless. The closing two lines of the first stanza imply that though the poet indulged himself in country pleasures, he has never been unmindful to perfect beauty of id eal spiritual love, which he always desired and has finally got in his present beloved. Obviously there is a shift from physical to spiritual love, sleeping to waking period, sensuous appearances to ideal reality and as if from platonic cave to the world of ight in the poet and his beloved. Here the poet seems to have touched the metaphysics of Plato. In his metaphysics, Plato at first takes something concrete such as man, but soon he leaps into abstract namely the Form of man. Similarly Donne also begins with physical love and soon he turns to Platonic or metaphysical love. The first stanza contains several Donnian elements. It opens abruptly with an explosive question. This abrupt colloquial beginning, which is so characteristic of Donne startles us and captures our attention. Another noticeable thing is that Donne swears his true relation l wonder by my troth. Here he is unconventional. Any of his contemporary of Elizabethan poets might swear to God, but Donne has not done it. Then there are the references of physical union and the use of imageries in the following three lines. The fourth line contains a legendary conceit,a legend that tells of seven young men of Ephesus who took refuge in a cave during the persecution of Diocletian and were entombed there. They were found alive two centuries later. Here Donne compares himself and his beloved with the seven sleepers. Here he is cynical when he utters the word did. Surely the word did includes the connotations of exual doing what did we ever do with the time? The second stanza begins with hail and celebration. The unconscious past of flesh is over and a new conscious spiritual relationship begins. So the speaker cerebrates the present. Now good morrow to our waking souls. He also makes declaration that their souls have also learnt not to spy one another. That the married women or men involve in extra-marital affair was a dominant theme in the Elizabethan and Jacobean literature. So, fear only works in sensual lovers as motivation for watching over each other, least the other should become unfaithful to his or her mate. But the speaker nd his beloved have overcome this fear and a peaceful satisfaction prevails their love. And for their faithful love they will control the temptations of other things. They love so faithfully and ardently that their love has the force to be merged into the universal love and to move out to become an every where. As spiritual lovers, the poet and his beloved are indifferent to earthly pleasures and possessions let the sea-lovers and map-lovers do what they like to do. The lovers want to be happy with their Joint world though they have their individual worlds but their individual worlds are fused into a single world. Now they are the Joint owners of a single world. Here in this stanza, we find the presence of imagery from the contemporary geographical world. That is to say the contemporary geographical interest of the explorers. The third stanza opens with endearing words from the speaker. The two lovers stand so closely that their respective faces are reflected in each others eyes. The simplicity of their heart is also reflected in their faces, which are conceived as two hemispheres of their world. But their world of love is so unearthly that its hemispheres are free from coldness and decay. They are not afraid of separation or break up of their relation, because What ever dyes, was not mixt equality. The ingredients of their love have been proportionately mixed and there is no ware and woof between them. They have love equally and proportionately. Thus the poem ends with the establishment of true friendship. After an abrupt beginning, there is calmness at last. The couple has rejected the country pleasures and entered into a true inter-dependent friendship. They have renounced the mundane world in order possess an unearthly world. Experience has thought them that the true happiness can be achieved through a mutual spiritual friendship. In the first stanza, there is the regret for past doings, in the second stanza the pleasure of discovering something in the last stanza, the prospect/hope of doing better/using the discovery. The abrupt beginning of the poem, the use of conceits form everyday life and myth in the first stanza, the geographical reference of stanza two, the use of scholastic philosophy in stanza three, and ultimately the emphasis of spiritual love continue to make it one of those poems of Donne which combine intellect and emotion. These above motioned qualities have made the poem get a certain place in honored, treasured lyrics written by John Donne.

Death Penalty Essay Example | Topics and Well Written Essays - 1000 words

Death Penalty - Essay Example The crimes that were punishable by death at this time were numerous unlike in the modern society, which has first degree murder as the major crime punishable by death. Under Hammurabi code, crimes such as adultery, petty robbery cases, wrongful accusation of individuals among others were regarded as capital offenses. This paper is a critical evaluation of death penalty as a component of criminal justice. Death Penalty Death penalty is a punishment in the criminal justice system, which has continued to attract criticism and support from various quarters such as human rights activists, religious groups as well as the society at large. As stated earlier, it is not a new phenomenon unique to the contemporary society as even the ancient societies applied it even though with little regard to human life. In this context, the manner in which it was applied especially in the Babylonian society under King Hammurabi showed that there was little will on the part of the authorities to offer convi cted criminals the chance to redeem themselves and reintegrate with the society (Brians, 1999). However, the contemporary society has tried to rectify this by limiting the punishable crimes to those of high gravity such as murder, genocide, treason among others. On the other hand, some of the countries around the world have decided, through legislation, to ban the death penalty altogether while others have remained adamant and still use it to date. Some of the countries that has banned it include and not limited to Argentina, Spain and Australia while those where it is still considered legal include and not limited to the US, China, Korea, Egypt, Japan among others. In total, it is believed that more than 139 countries worldwide have banned its use. The most conventional methods of execution to date include and not limited to; hanging, being shot by firing squad, electric chair, gas chamber among others (Bedau, 2004). This is in contrast with the ancient methods of execution, which can be termed as primitive, inhumane as they were more bent towards making the convicted criminal experience pain and humiliation. In fact, some sources indicate that such societies believed in the ‘tit for tat’ and the ‘an eye for an eye’ principles. Convicts were either beaten to death, burned alive, crucifixion while others were drowned in water (Brians, 1999). Recidivism is a major concern for proponents of death penalty (McCafferty, 2009). This is the habit of convicted criminals returning into crime once they are released from prison after completing their jail terms. It is for this reason that death penalty is perceived as a permanent way of incapacitating the criminals from ever committing other felonies. Opponents on the other hand claim that instead of killing the culprits, they should be sentenced to life imprisonment without parole. However this may sound convincing, it does not escape the minds of the proponents that jailing a criminal only lim its his or her rights to freedom but this does not mean that they lose contact with the outside world. In this context, it is true to say that the criminals can still plan with the help of accomplices especially where criminal gangs are involved. This may turn out to

Visual Anaysis Essay Example | Topics and Well Written Essays - 500 words

Visual Anaysis - Essay Example One of the objectives of modern art is to express the artist’s thoughts and emotions by following what is deemed right by the artist wherein to cubists, form alone defines the subject thus, the use of geometric figures to bring out the form of the subject matter. Juan Gris, a Spanish cubist artist portrayed a self portrait of the bench marker of the cubism period, Pablo Picasso, in the vantage point of their contemporaries with the same vision in his work entitled Portrait of Pablo Picasso (The Art Institute of Chicago). The general form of the painting shows a man seated and exhibits him to be well dressed though not very recognizable for a viewer to really be sure of what details one can decipher from the painting. As what would have been expected from a cubist, Gris used a lot of lines, cubes and other geometric figures to bring out the three dimensional view of his subject however, distorting the real appearance of the image. Being called analytical, the cubist painters we re said to have analyzed their subject to come up with the products like the aforementioned art work and among others, Mandolin and Clarinet by Pablo Picasso and Armored Train in Action by Gino Severini.

Mexico Case Study Example | Topics and Well Written Essays - 1750 words

Mexico - Case Study Example Large numbers of multinational automobile manufacturers are either shifting their base from Canada to Mexico or are limiting their operations in Canada and expanding the same in Mexico (Althaus â€Å"Mexico Sets Auto Production Record, Aims for More†). Mexico has gained significant attention of automotive producers, as a result of several factors that deliver geographical and other competitive advantages to manufacturers. These advantages include extremely low wage rate, free trade agreements with approximately 45 countries across the globe, well-developed transport system with year-round ice free coastal ports and high level of financial incentives from Mexican government. Owing to these features, global automakers announced investment of $7 billion in Mexico as opposed to $750 million in Canada. Mexico’s stable and lucrative business environment has caused most automakers to adopt global strategy for business expansion (Keenan â€Å"Mexico races ahead in auto industry as Canada stalls†). Contextually, it is imperative to comprehend the distinction between international and global strategies even though they are frequently used interchangeably. These differentiating factors are degree of central involvement and coordination in strategic activities, degree of standardization in products and responsiveness towards local business environment and strategic integration and competitive steps. Since internationalization in Mexico is mainly about achieving competitiveness in production and operations, almost all companies adopt global strategy (Twarowska and Kakol 1005-1011). Under the purview of global strategy, most automakers ensure and exercise central control over production in Mexico and as automotive industry is about ensuring standardization in products, global strategy is most effective herein. Unlike international

Emotion Recognition From Text-a Survey

Emotion Recognition From Text-a Survey Ms. Pallavi D. Phalke , Dr. Emmanuel M. ABSTRACT Emotion is a very important facet of human behaviour which affect on the way people interact in the society. In recent year many methods on human emotions recognition have been published such as recognizing emotion from facial expression and gestures, speech and by written text. This paper focuses on classification of emotion expressed by the online text, based on predefined list of emotion. The collection of dataset is the basic step, which is collected from the various sources like daily used sentences, user status from various social networking websites such as  facebook and twitter. Using this data set we target only on the keywords that show human emotions. The targeted keywords are extracted from the dataset and translated into the format which can be processed by the classifier to finally generate the Predicting model which is further compared by the test dataset to give the emotions in the input sentences or documents. Keywords— Affective Computing, Classification, Document Categorization, Emotion Detections. INTRODUCTION Recently much research is going on in emotion recognition domain. Recognition of emotions is very useful to human-machine communication. Many kinds of the communication system can react properly for the humans emotional actions by applying emotion recognition techniques on them. These systems include dialogue system, automatic answering system and robot. The recognition of emotion has been implemented in many kinds of media, such as image, speech, facial expressions, signal, textual data, and so on. Text is the most popular and main tool for the human to convey messages, communicate thoughts and express inclination. Textual data make it possible for people to exchange opinions, ideas, and emotions using text only. Therefore the research for recognizing from the textual data is valuable. Keyword-based approach to the proposed system since the keyword-based approach shows high recognizing accuracy for emotional keywords. Interaction between humans and computers has been increased with increase in development of information technology. Recognizing emotion in text from document or sentences is the first step in realizing this new advanced communication which includes communication of information such as how the writer/speaker feels about the fact or how they want the reader/listener to feel. Analyzing text, detecting emotions is useful for many purposes, which includes identifying what emotion a newspaper headline is trying to evoke, identifying users emotion from their statuses of different social networking sites, devising dialogue systems that respond appropriately to different emotional states of the user and identifying blogs that express specific emotions towards the topic of interest. List of emotions and words that are indicative of each emotion is likely to be useful in identifying emotions in text because, many times different emotions are expressed by different words. For example cry and glo omy are indicative of sadness, boiling and shout are indicative of anger, yummy and delightful indicate the emotion of joy. To capture emotion from text document we require the classification which aims at presume the emotion conveyed by the documents based on predefined lists of emotion, such as Joy, Anger, Fear, Disgust, Sad and Surprise. This emotion recognition approach is mainly focused on two main tasks. 1) The test data that is text document collected from any news articles, user statuses from different social networking sites etc. required for understanding the emotions evoked by words. This is because a different word arouses different emotions comprehended from our day to day experiences. For this purpose, need is to enhanced dictionary with emotion word from ISEAR, WorldNet Affect to improve in result. 2) Need for text normalization to handle negation, since the scope of words is larger in this scenario, the usage of words and their diverted form is large too. So these problems need to be solved properly. The next part of this paper is organised as follows: Section II discusses a survey of emotion detection from text, Section III describes different algorithms on different datasets for emotion recognition, Section IV briefly compares proposed work followed by experimental study with result in section V and Section V concludes the paper. THE SURVEY OF EMOTION DETECTION FROM TEXTS Definitions about emotion, its categories, and their influences have been an important research issue long before computers emerged, so that the emotional state of a person may be inferred under different situations. In its most common formulation, the emotion detection from text problem is reduced to finding the relations between specific input texts and the actual emotions that drives the author to type/write in such styles. Intuitively, finding the relations usually relies on specific surface texts that are included in the input texts, and other deeper inferences that will be formally discussed below. Once the relations can be determined, they can be generalized to predict others’ emotions from their articles, or even single sentences. At the first glance, it does not seem to involve so many difficulties. In real life, different people tend to use similar phrases (i.e. â€Å"Oh yes!†) to express similar feelings (i.e. joy) under similar circumstances (i.e. achieving a goal); even they native languages are different, the mapping of such phrases from each language may be obvious. More formally, the emotion detection from text problem can be formulated as follows: Let E be the set of all emotions, A be the set of all authors, and let T be the set of all possible representations of emotion-expressing texts. Let r be a function to reflect emotion e of author a from text t, i.e., r: A Ãâ€" T → E and the function r would be the answer to our problem. The central problem of emotion detection systems lies in that, though the definitions of E and T may be straightforward from the macroscopic view, the definitions of individual element, even subsets in both sets of E and T would be rather confusing. On one hand, for the set T, new elements may add in as the languages are constantly evolving. On the other hand, currently there are no standard classifications of â€Å"all human emotions† due to the complex nature of human minds, and any emotion classifications can only be seen as â€Å"labels† annotated afterwards for different purposes. As a result, before seeking the relation function r, all related research firstly define the classification system of emotion classifications, defining the number of emotions. Secondly, after finding the relation function r or equivalent mechanisms, they still need to be revised over time to adopt changes in the set T. In the following subsections, we will present a classification of emotion detection methods proposed in the literature, based on how detection are made. Although they can all be classified into content-based approaches from the point of view of information retrieval, their problem formulation differs from each other: 1. Keyword-based detection: Emotions are detected based on the related set(s) of keywords found in the input text; 2. Learning-based detection: Emotions are detected based on previous training result with respect to specific statistic learning methods; 3. Hybrid detection: Emotions are detected based on the combination of detected keyword, learned patterns, and other supplementary information; Besides these emotion detection methods that infer emotions at sentence level, there has been work done also on detection from online blogs or articles [1][2]. For example, though each sentence in a blog article may indicate different emotions, the article as a whole may tend to indicate specific ones, as the overall syntactic and semantic data could strengthen particular emotion(s). However, this paper focuses on detection methods with respect to single sentences, because this is the foundation of full text detection. A. KEYWORD-BASED METHODS Keyword-based methods are the most intuitive ways to detect textual emotions. To approximate the set T, since all the names of emotions (emotion labels) are also meaningful texts, these names themselves may serve as elements in both sets of E and T. Similarly, those words with the same meanings of the emotion labels can also indicate the same emotions. The keywords of emotion labels constitute the subset EL in set T, where EL also classifies all the elements in E. The set EL is constructed and utilized based on the assumption of keyword independence, and basically ignores the possibilities of using different types of keywords simultaneously to express complicated emotions. Keyword-based emotion detection serves as the starting point of textual emotion recognition. Once the set EL of emotion labels (and related words) is constructed, it can be used exhaustively to examine if a sentence contains any emotions. However, while detecting emotions based on related keywords is very straightforward and easy to use, the key to increase accuracy falls to two of the pre-processing methods, which are sentence parsing to extract keywords, and the construction of emotional keyword dictionary. Parsers utilized in emotion detection are almost ready-made software packages, whereas their corresponding theories may differ from dependency grammar to theta role assignments. On the other hand, constructing emotional keyword dictionary would be naval to other fields [3]. As this dictionary collects not only the keywords, but also the relations among them, this dictionary usually exists in the form of thesaurus, or even ontology, to contain relations more than similar and opposite ones. Semi-automatic construction of EL based on WorldNet-like dictionaries is proposed in [4] and [5]. As was observed in [6], keyword-based emotion detection methods have three limitations described below. 1) AMBIGUITY IN KEYWORD Though using emotion keywords is a straightforward way to detect associated emotions, the meanings of keywords could be multiple and vague. Except those words standing for emotion labels themselves, most words could change their meanings according to different usages and contexts. It is not feasible to include all possible combinations into the set EL. Moreover, even the minimum set of emotion labels (without all their synonyms) could have different emotions in some extreme cases such as ironic or cynical sentences. 2) INCAPABILITY OF RECOGNIZING SENTENCES WITHOUT KEYWORDS As Keyword-based approach is totally based on the set of emotion keywords, sentences without any keywords would imply like they don’t contain any emotions at all, which is obviously wrong. 3) LACK OF LINGUISTIC DATA Syntax structures and semantics also affect on expressed emotions. For example, â€Å"He laughed at me â€Å"and â€Å"I laughed at him† would suggest different emotions from the first person’s point of view. Therefore, ignoring linguistic information also create a problem to keyword-based methods. B. LEARNING-BASED METHODS Researchers using learning-based methods attempt to formulate the problem differently. The original problem that determining emotions from input texts has become how to classify the input texts into different emotions. Unlike keyword-based detection methods, learning-based methods try to detect emotions based on a previously trained classifier, which apply various theories of machine learning such as support vector machines [7] and conditional random fields [8], to determine which emotion category should the input text belongs. However, comparing the satisfactory results in multimodal emotion detection [9], the results of detection from texts drop considerably. The reasons are addressed below: 1) DIFFICULTIES IN DETERMINING EMOTION INDICATORS The first problem is, though learning-based methods can automatically determine the probabilities between features and emotions, learning-based methods still need keywords, but just in the form of features. The most intuitive features may be emoticons, which can be seen as author’s emotion annotations in the texts. The cascading problems would be the same as those in keyword-based methods. 2) OVER-SIMPLIFIED EMOTION CATEGORIES Nevertheless, lacking of efficient features other than emotion keywords, most learning-based methods can only classify sentences into two categories, which are positive and negative. Although the number of emotion labels depends on the emotion model applied, we would expect to refine more categories in practical systems. C. HYBRID METHODS Since keyword-based methods with thesaurus and naà ¯ve learning-based methods could not acquire satisfactory results, some systems use a hybrid approach by combining both or adding different components, which help to improve accuracy and refine the categories. The most significant hybrid system so far is the work of Wu, Chuang and Lin [6], which utilizes a rule-based approach to extract semantics related to specific emotions, and Chinese lexicon ontology to extract attributes. These semantics and attributes are then associated with emotions in the form of emotion association rules. As a result, these emotion association rules, replacing original emotion keywords, serve as the training features of their learning module based on separable mixture models. Their method outperforms previous approaches, but categories of emotions are still limited. D. SUMMARY AND CONCLUSIONS As described in this section, much research has been done over the past several years, utilizing linguistics, machine learning, information retrieval, and other theories to detect emotions. Their experiments show that, computers can distinguish emotions from texts like humans, although in a coarse way. However, all methods have certain limitations, as described in the previous subsections, and they lack context analysis to refine emotion categories with existing emotion models, where much work has been done to put them computationalized in the domain of believable agents. On the other hand, applications of affective computing would expect more refined results of emotion detection to further interact with users. Therefore, developing a more advanced architecture based on integrating current approaches and psychological theories would be in a pressing need. III. ALGORITHMS USED IN EMOTION RECOGNITION A brief summary of the various works for emotion recognition discussed in this paper are presented in Table1. Table 1: Results and feature-set comparison of algorithms IV.EMOTION RECOGNITION IN SOCIAL COMMUNICATION The block diagram of the emotion recognition system studied in this paper is depicted in Figure 1.It contains three main modules: Affective communication unit, Data Aggregator, Emotion Recognition Engine and recognized emotion class as an output. Figure 1 : Block diagram of emotion recognition system for Affective communication AFFECTIVE COMMUNICATION UNIT Affective Communication Unit is nothing but the users account in any social networking site (tweeter or facebook). This system take input from these two social networking sites. DATA AGGREGATOR Data Aggregator collects user tweets and status from tweeter and facebook. These tweets/status serve as an input to Emotion Recognition Engine. EMOTION RECOGNITION ENGINE Emotion Recognition Engine including Bayesian Network classifier categorizes incoming data into 3 types of emotions: happiness, sadness, and neutral, because this system mainly focuses on finding stress level of user. It is broken up into 2 major phase: Training Phase and Testing Phase. Training phase consist of five important parts: The Training Dataset, Keyword Extraction, Keyword conversion, Training Model and Predicting Model. Before it generate the predicting model or file, training phase get the training dataset from which it extracted the keyword from the emotion training date, and convert the keyword using keyword conversion into the format that can be processed by the classifier in the Training Model. Testing phase which is also called predicting phase consist of Testing dataset, Keyword extraction, Keyword conversion and predict model. The testing phase extract the Keyword from the given sentence, which was the input from the keyboard and then translate the keyword (word of natural language) using the Keyword conversion into the format that can be processed and then we compare it with a predicting file in predict module and finally gives the output as appropriate emotion expressed by the text. VI.CONCLUSION The proposed system is able to recognize the happy and sad state of a person from his tweets posted on tweeter from his mobile. The experimental results Shows that the we get better accuracy using Naive Bayes classifier than that of Support Vector Machine. VII. REFERENCES [1] 2. Tim M.H. Li, Michael Chau, Paul W.C. Wong, and Paul S.F. YipA Hybrid System for Online Detection of Emotional Distress PAISI 2012, LNCS 7299 Springer-Verlag Berlin Heidelberg 2012M, 73–80. [2] Abbasi, A., Chen, H., Thoms, S., Fu, T.: â€Å"Affect Analysis of Web Forums and Blogs Using Correlation Ensembles.† IEEE Transactions on Knowledge and Data Engineering (2008) ,1168–1180. [3] T. Wilson, J. Wiebe, and R. Hwa, â€Å"Just how mad are you? Finding strong and weak opinion clauses,† Proc. 21st Conference of the American Association for Artificial Intelligence Jul. 2007, 761-769. [4] D. B. Bracewell, â€Å"Semi-Automatic Creation of an Emotion Dictionary Using WordNet and its Evaluation,† Proc. IEEE conference on Cybernetics and Intelligent Systems, IEEE Press, Sep. 2008, 21-24. [5] J. Yang, D. B. Bracewell, F. Ren, and S. Kuroiwa, â€Å"The Creation of a Chinese Emotion Ontology Based on HowNet†, Engineering Letters, Feb. 2008,166-171. [6] C.-H. Wu, Z.-J. Chuang, and Y.-C. Lin, â€Å"Emotion Recognition from Text Using Semantic Labels and Separable Mixture Models,† ACM Transactions on Asian Language Information Processing Jun. 2006, 165-183. [7] Z. Teng, F. Ren, and S. Kuroiwa, â€Å"Recognition of Emotion with SVMs,† in Lecture Notes of Artificial Intelligence Eds.Springer, Berlin Heidelberg, 2006,701-710 . [8] C. Yang, K. H.-Y. Lin, and H.-H. Chen, â€Å"Emotion classification using web blog corpora,† Proc. IEEE/WIC/ACM International Conference on Web Intelligence. IEEE Computer Society, Nov. 2007, 275-278. [9] C. M. Lee, S. S. Narayanan, and R. Pieraccini, Combining Acoustic and Language Information for Emotion Recognition, Proc. 7th International Conference on Spoken Language Processing (ICSLP 02), 2002, 873-876. [10]http://www.affectivesciences.org/reserachmaterial [11] http://www.weka.net.nz/

Irish Potato Famine Essay -- essays research papers

In the early 1800s life in Ireland wasn’t easy, Irish citizens got by day to day by farming and relying on the potato. The potato was their main source of food and money. With out the potato the Irish would have nothing. No one was prepared for what was about to happen in 1845, the beginning of the Great Irish Potato Famine.   Ã‚  Ã‚  Ã‚  Ã‚  The Irish Potato Famine was the worst tragedy in the history of Ireland. The outcome of the famine would result in hundreds of thousands dead, an failure of the economy in Ireland, and millions of emigrants forced to leave their home and country just to try to survive. The famine would effect countries other than Ireland as well. Some of these countries included England, America, Canada, and Australia.   Ã‚  Ã‚  Ã‚  Ã‚  The next five years, almost all Irish citizens, would have the hardest struggle that they would ever face. It would tear families apart, destroy peoples lives, and cause large financial losses to landowners.   Ã‚  Ã‚  Ã‚  Ã‚     Ã‚  Ã‚  Ã‚  Ã‚   In the early to mid 1800s Ireland was a very poor and difficult place to live. Most of the land was owned by landowners that lived in England and rented their land out to Irish citizens. The owners had almost no interest in their land and property in Ireland. They only cared about getting their money from their renters. The rents were overpriced and living costs in Ireland were also extremely high. The living conditions for the renters in Ireland were horrible, with one-room houses that were expected to shelter whole families. Another problem with the country was that over 70% of the population was illiterate.   Ã‚  Ã‚  Ã‚  Ã‚  The renters would use their land to farm potatoes because they were cheap, easy to grow, full of vitamins, and you could grow a lot in a small area and in poor farming conditions. The whole country relied on the crop of potatoes as their source of food and income.   Ã‚  Ã‚  Ã‚  Ã‚  In the mid 1800s there were many seasons that produced poor crops, and in some cases no potatoes at all. These seasons were taken lightly, and just thought to be bad crop seasons. After these bad seasons, farmers became upset and began to grow poorer quality potatoes known as ‘Lumper potatoes’ or ‘Horse potatoes’ instead of the stronger healthier potatoes. ... ...money to their family so that they could escape Ireland and start a new life in America. Those whose lives became bad and worsened by the immigration were too ashamed to talk to their family and discontinued contact with family back in Ireland.   Ã‚  Ã‚  Ã‚  Ã‚  In conclusion the potato famine effected not only those who lived in Ireland, but those in America too. The people that survived the crisis eventually returned their lives back to normal and were not physically harmed but rather emotionally scared with memories of wondering how much longer they were going to live. Also they remembered the whole scene with dead bodies every where and villages of which every resident was sickened and dying.   Ã‚  Ã‚  Ã‚  Ã‚  The Americans were effected by this epidemic with the introduction of many diseases introduced to their country. Especially those in the New England area of the country, where most of the immigrants arrived who had seen how the famine over seas had such an impact on millions of people’s lives.   Ã‚  Ã‚  Ã‚  Ã‚  Over all many lives were lost, many lives changed, all due to the only crop that the Irish relied on†¦the potato.