Using A Part Of Speech Tagger

Natural language processing: Natural language is the embodiment of human cognition and human intelligence. Natural language processing (NLP) is a cross disciplinary field in computer science and linguistics where a computer can generate and understand human speech. It is the ability of a computer program to understand human speech as it is spoken. NLP is a component of artificial intelligence (AI).The development of NLP applications is challenging because computers traditionally require humans to “speak”

Abstract: I have been working as a Research Assistant to Prof. H R Rao and PhD student Jae Ung Lee, from August 2015, on one of their research papers on sentiment analysis during the Boston bombing incident. The tweets regarding that incident were collected and provided, on which I had to perform the analysis to categorize the tweets based on a set of emotions. Data filtering was done using a keyword search method and sentiment score was generated using the Linguistic Inquiry and Word Count (LIWC) software. I used the tools like NodeXL, Nvivo and TAPOR as part of this data analysis work. The refined list of keywords were put in different groups of emotions. The data after categorization was studied to find out how the emotions of people changed as each day passed after the incident and with every major developments as part of the incident. Statistical analysis was also done as part of this for each of the three emotions under study – Anger, Anxiety and Sadness. ANOVA results were generated to study the mean change in emotions among the two different categories – tweets with event related keywords and tweets without event related keywords. The second part of the research was focused on the shift in sentiment among the users, which would give the impact of a particular tweet (Boston Police Department tweet). For this analysis, a set of users who retweeted that particular tweet were taken and a constant number of tweets regarding this were collected for all the days associated

Social media has emerged as the most powerful form of communication these days. The ability to voice an opinion from anywhere over anything has coined and popularized the term ‘Global Citizen. Many social platforms available these days provide the avenues for the companies and organization to make a brand for themselves by directly engaging with their customers. If you don’t like some brand you bought or a movie you just watched, login on Twitter or Facebook and tell the world. These platforms provide an access into what is currently trending or making the stir at the moment. Thus, there is a huge repository of opinionated data that if harnessed strategically can help to solve problems ranging from what product to invest into complex business

Arabic is an interesting language because it is different from region to region. Elizabeth Pietanza states, there are “three different forms: classical, modern standard, and spoken” (Pietanza 2001:1078). The Classical form of Arabic is based on the Islamic religious book, the Quran. Most children living in Middle Eastern countries are taught to read and write using the Modern Standard form of Arabic, however the spoken language is different from country to country. Iraqi spoken language is different from the

As in our study, LDA topics has improved accuracy of finding the keywords for different topics.In this work we examine the social aspects of food tweeting behavior, and provide some support to the social affinity that is not local in geographic sense. There have been several recent studies that probe the viability of public health surveillance by measuring relevant textual signals in social media.Prier, K.W.Smith, M.S.Giraud-Carrier, C. L. Hanson[5] examine all words people use in online reviews, and draw insights on correlating terms and concepts that may not seem immediately relevant to the hygiene status of restaurants. The work draws from the rich body of research that studies online reviews for sentiment analysis based on few research papers.

Social media has become prominently popular. Tens of millions of users login to social media sites like Twitter to disseminate breaking news and share their opinions and thoughts. For businesses, social media is potentially useful for monitoring the public perception and the social reputation of companies and products. Despite great potential, how bad news about a company influences the public sentiments in social media has not been studied in depth. The aim of this study is to assess people’s sentiments in Twitter upon the spread of two types of information: corporate bad news and a CEO’s apology. We attempted to understand how sentiments on corporate bad news propagate in Twitter and whether any social network feature facilitates its

Abstract— Resumes are shared online significantly due to the widespread presence of the job websites. Normally, resumes do not have a structured format, which makes it difficult for the organizations to parse and extract information automatically. In this research, a novel methodology is presented for converting the relevant information from resume and the corresponding sentiments from Twitter to a structured format. Initially, the relevant information is extracted from the resume files. The behavior pattern from Twitter for each candidate is also extracted using sentiment analysis on their posts. Finally, this is combined with the extracted information from the resume and is converted to JSON format to obtain a structured

The syllable and grammar for the second era are subject to be studied in commensuration with the phonology of the Arabic language in a multi-linguistic scenario of community. This is due to the fact that most of the communications at the regional level are oral by nature. Thus, the features of grammar and syllable cannot be ignored. It has been identified by Kahn (1976) that a mere hypothetical word “atkin” is not used and identified in English but without a syllable. The arrangements of the letters neither assumed later and former use of letters. But, there is a central place exists in few words; such as, “Caltex”. Because of this, such types of words are not found easily in the English literature because of the placement and arrangements of the letters in the word. The next matter of consideration is the segmental phonology rules; assimilation, length, neutralization, affrication, use of consonant and vowels. These rules are considered in alignment with the syllables of the word. A Syllable can use the segmental phonology rules; thus, the element of pharyngealization is observed rather than the use of other rules and practices. Moreover, the elements of rules are determined through the use of various functions; hence, there is obstruent exist in English which are the factors talk about the placement of the letter or the word. Lastly, the element of oral use and breaks of words is assumed as the innate centrifugal phenomenon of Arabic language in

Arabic is usually ranked among the top six of the world's major languages. As the language of the Qur'an, the holy book of Islam, it is also widely used throughout the Muslim world. It belongs

With the development of culture, communication has become more and more important. In order to understand each other , most people focus on how to express themselves efficiently, they use language which is clear and certain. It seems that in this way the meaning can be expressed accurately, while fuzzy words must be avoided. But a lot of facts have proved that the uses of fuzzy words is very common in our daily communication.

Now, there are different approaches to develop a tagger like Rule-based approach, Stochastic or probability approach and transformation-based approach. A simple rule-based tagger automatically acquires its rules and tags with accuracy. ENGTWOL, an English Rule based tagger is considered to be one of the most important tagger of the models based on constraint grammar architecture. After the rule based approach, the statistical or stochastic approach is used to develop language models to disambiguate a word sequence by assigning the most probable tag sequence. In a simplest stochastic tagger, each word is assigned its most probable tag. But, the disadvantage of such tagger is that it may generate a valid tag for a word as well as unacceptable

The current technological age that uses the social media has led various problems in writing and receiving emails/texts. The biggest problem is not getting any part of a message from the text or email; understanding of the message is the greatest problem. This can be attributed to the receiving of incomprehensible and poorly arranged words and messages. The problem of using slang in writing and receiving texts is a menace. The use of such slang terms like SMH (shaking my head) among others, makes communication unofficial and only understood by a certain group of people (Heather & Graves, 2012).

These HMMs are used by the decoder to match audio features with a specific phoneme. The decoder keeps track of the set of phonemes found until the audio reaches a pause, and then the system searches the dictionary to construct a set of words that match the phonemes recognized. The dictionary is simply the set of words to be recognized and the phonemes that make them up. The language model is used in this process as well. Since the language model contains the probabilities of words and word sequences it is used to constrict the dictionary search to words that are more probable to occur [3]. Therefore, the word or words constructed with the highest probability are then the next words in the transcript [4]. Figure 1 outlines the high level process of a speech recognition engine.

The communication in the message may have poor grammar/ or spelling. Odd phrases and typo errors are a common characteristic. The attackers are however, mastering the art of proficiency hence such mistakes are becoming less common. There are however, such careless mistakes being, made and such may be an im-portant lead to the user.

We obtain competitive results on a real-world 4-language task: for our best JSM system, an F1 value of 97.2% is obtained, compared to a F1 value of 95.2% obtained with a state-of-the-art Support Vector Machine (SVM).