Agile-Model-Based Sentiment Analysis From Social Media

Agile-Model-Based Sentiment Analysis From Social Media

Tarandeep Kaur Bhatia (Chandigarh Group of Colleges, India)
DOI: 10.4018/978-1-5225-5222-2.ch002

Abstract

To study state-of-the art associated to Twitter mining replica as well as prognostic analytic by means of Agile Software Engineering. To recognize sentiment analysis by means of agile knowledge. To obtain as well as analysis given repository for classifying sentiments into positive, negative and neutral emotions. Analysing of all the tweets obtained from the twitter keywords as positive, negative or neutral opinions and comparing all the keywords to judge which keyword is better, there is a requirement to improve from the conventional ways of sentiment analysis. This paper emphasizes on the implementation of an algorithm for automatic classification of text into positive, negative or neutral by fetching the live tweets from twitter server by using twitter API. Graphical representation of the sentiment for the purpose of comparison in the form of pie chart and bar graph. Scan the twitter and fetching the Live Tweets from Twitter server using Twitter4J Advance Java Interface and implementing the Stanford NLP Library (Natural Language Parsing) using Advance Java for classifying the tweets into positive, negative and neutral tweets.
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Introduction

The difficulty in sentimental_analysis is categorizing the polarity of a specified manuscript at a sentence, document or feature/aspect level whether the uttered belief in a sentence, document and feature/aspect is positive, negative or neutral. (Ågerfalk, Pär, Fitzgerald, 2006). Twitter is a public networking as well as micro_blogging examination that permit clients to post real_time message, known as tweets. Tweets are tiny posts, limited to 140 lettering in extent. (Charalabidis, Yannis, Triantafillou, Karkletsis, Loukis, 2012). Due to the natural world of this micro_blogging examination, public apply acronyms; create spelling_mistakes, make use of emoticons as well as extra fonts that state extraordinary significance. Subsequent is a concise vocabulary linked by means of tweets. (Colbaugh, Richard and Galss, 2011).

  • Emoticons: They are facial_expressions, diagrammatically signifying by means of punctuation as well as letters; they state the consumer’s humor. (Hogenboom, Alexander, Bal, Fransincar, Bal, Joung and Kaymak, 2013).

  • Target: Consumers of Twitter make use of the @ sign to refer to further consumers on the micro_blog. Referring to further consumers in this way robotically aware them. (Yi, Jeonghee, Nasukawa, Bunescu, Niblack, 2003).

  • Hash_Tags: Consumers frequently make use of hash_tags to spot subject. This is first and foremost prepared to enlarge the visibility of their tweets. (Naveed, Nasir, Gottron, Kunegis, Alhadi, 2011).

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Proposed System And System Design

Methodology

  • 1.

    To study state-of-the art associated to Twitter mining replica as well as prognostic analytics by means of Agile Software Engineering.

  • 2.

    To recognize sentiment analysis by means of agile knowledge.

  • 3.

    To obtain as well as analysis given repository for classifying sentiments into positive, negative and neutral emotions.

Procedure

This study job is paying attention on the prognostic investigation as well as alliance of consumer account through their timeline. (Rosenthal, Sara, Nakov, Kiritchenko, Mohamad, Ritter, Stoyanov, 2015). There are numerous parts of planned job as well as execution by which the withdrawal of live social_media as well as particular characteristic are interconnected for the connection in addition to bond (Prabowo, Rudy, Thelwall, 2009). By means of this strategy, the outline of whichever consumer can be straightforwardly obtained from numerous social_media as well as after that the chance or connection features through additional functions can be completed. (Whitworth, Biddle, 2007).

In this study job, the sentiment facts investigation as well as forecast is prepared on the large data obtained during live tweets. By means of forecasting tools in addition to algorithms, the successful as well as correct outcomes shall be calculated. (Hao, Ming, Rohrdantz, Janetzko, Dayal, Keim, Haug, Hsu, 2011).

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