Monolingual Word Alignment Model For Retrieving

About.com. N.p., n.d. Web. 22 Aug. 2011. . In the year 2008 there were 426,000 computer programmers employed. Computer programmers write the program the computer uses to function. The computer programmer follows the specifications given to them by the computer software engineer. Specialized knowledge and experience with a language or operating system could lead to a computer programmer becoming a computer software engineer. Programming skills and programming experience is highly valued in this field. Computer programmers must constantly update their skills to keep up with changing technology. According to the U.S. Bureau of Labor Statistics predicts, Employment of computer programmers, is expected to decline slowly through 2018.

Norton (Ed.). (2006). Computing Fundamentals. [University of Phoenix Custom Edition e-Text]. New York, New York: McGraw-Hill. Retrieved January 21, 2011, from CIS105 - Computers-Inside and Out.

College of Information Systems & Technology IT/210 Version 5 Fundamentals of Programming With Algorithms and Logic

Opinion mining (also known as sentiment analysis) focuses mainly on two things, first identifying whether the text is subjective or objective and then determining the polarity of the subject [3]. Opinion words in the text mostly contribute towards the subjectivity of the text. These opinion words together inform about the emotional context of the text. Objective words contribute towards the factual construct of the text. There are three most common approaches used for determining the sentimental content of a text

.........................................................................................................11 SAMPLE SCREEN SHOTS...........................................................................................................................................12 Request Entry Screen 1 ......................................................................................................................................12 Request Entry Screen 2 ......................................................................................................................................13 Overall Work Queue ..........................................................................................................................................14 Departmental Work Queue ................................................................................................................................15 Branch Manager Menu ......................................................................................................................................16 Return Record Screen ........................................................................................................................................17 Property Risk Evaluation Screen .......................................................................................................................18 ORGANIZATION DESIGN CONSIDERATIONS

Makerere University, Faculty of Computing and Information Technology, P.O. Box 7062, Kampala, Uganda, East Africa jlubeg@cit.mak.ac.ug 2 Department of Computer Science, University of Reading, P.O. Box 225, Whiteknights, Reading, Berkshire, RG6 6AY, United Kingdom shirley.williams@reading.ac.uk

It is used to understand the emotion conveyed in a textual message. It involves identifying the opinion, extracting the features or objects for which the opinion is expressed and then categorizing the opinion as a positive, negative or neutral and thus assigning it a polarity (Liu 2010). The growth in social media provides a wider platform which has allowed for an abundance in the expression of opinions, including product reviews, blogs, and discussion groups or simply as comments and tweets. Different techniques for sentiment analysis use Natural Language processing and machine learning perform Sentiment analysis on the large quantities of data available on the social media networks.

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1Research Scholar, 2Associate Professor, Department of Computer Science, AIM & ACT, Banasthali University, Banasthali-304022, email: jangid.divya@gmail.com

Yang Peng, Melody Moh, Teng-Sheng Moh, Efficient Ad- verse Drug Event Extraction using Twitter Sentiment Analysis , in this they proposed a simple, efficient pipeline for retrieving ADEs. Any selected drug should have been in the market for more than ten years. Following this rule, there are sufficient number of tweets exist for any selected drug. Drug related classification is done on preprocessed Data. Sentimental Anal- ysis. 5 times

In the sentiment extraction phase, the candidate’s name extracted from the resume was used to search on Twitter to find his/her profile. The candidate’s tweets are retrieved for further processing using Tweepy package and its API [13]. Then, these tweets are analysed to extract the sentiments for each candidate. This is done by using Textblob package which is a popular natural language processer [14].

First, we derive the collective opinion $O$ of a whole data sample $D$, using a SA method existing in the literature. Specifically, we adopt in this work the method SACI cite{jws2015}. SACI is relevant to our goal since it was originally proposed for estimating efficiently collective sentiment on data samples, instead of aggregating the sentiment derived for each individual document. Further, the authors demonstrated that SACI is more effective in estimating the collective opinion than aggregation-based SA methods. SACI represents $O$ as a distribution probability among the sentiment classes positive, negative and neutral. Thus, we split $D$ into time units of equal size (e.g., days, weeks, months). Then, we estimate the collective opinion $O_t$ using only the posts belonging to each distinct time unit $t$. Finally, we perform a visual inspection on the derived distributions. The more dynamic a domain, the more different are opinions estimated on distinct time units.looseness=-1

Submitted in partial fulfillment of the requirements for the degree of Bachelor of Engineering in Computer Engineering

Abstract-Twitter has become one of the most important communication channels with its ability providing the most up-to-date and newsworthy information. Considering wide use of twitter as the source of information, reaching an interesting tweet for user among a bunch of tweets is challenging. A huge amount of tweets sent per day by hundred millions of users, information overload is inevitable. For extracting information in large volume of tweets, Named Entity Recognition (NER), methods on formal texts. However, many applications in Information Retrieval (IR) and Natural Language Processing (NLP) suffer severely from the noisy and short nature of tweets. In this paper, we propose a novel framework for tweet segmentation in a batch mode, called HybridSeg by splitting tweets into meaningful segments, the semantic or context information is well preserved and easily extracted by the downstream applications. HybridSeg finds the optimal segmentation of a tweet by maximizing the sum of the stickiness scores of its candidate segments. The stickiness score considers the probability of a segment being a phrase in English (i.e., global context) and the probability of a segment being a phrase within the batch of tweets (i.e., local context). For the latter, we propose and evaluate two models to derive local context by considering the linguistic features and term-dependency in a batch of tweets, respectively. HybridSeg is also designed to iteratively learn from confident segments as pseudo

Special thanks to our families whose courage and support for us to follow the computer science department and the lecturers in computer science department. Thanks to our supervisor Dr. Nazar saaid for his support through the graduation project.