Tweelyzer. An Approach to Sentiment Analysis of Tweets (eBook, PDF)
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The ongoing trend of people using microblogging to express their thoughts on various topics has increased the need for developing computerised techniques for automatic sentiment analysis on texts that do not exceed 200 characters. Twitter is a "micro-blogging" social networking site that has a large and rapidly growing base of users. Twitter's tweets or messages are limited to 140 characters. Because of this limitation, it is more difficult to express sentiment and the classification of the tweets is difficult as well. Sentiment analysis can be done on two types: emotion and opinion. This rese...
The ongoing trend of people using microblogging to express their thoughts on various topics has increased the need for developing computerised techniques for automatic sentiment analysis on texts that do not exceed 200 characters. Twitter is a "micro-blogging" social networking site that has a large and rapidly growing base of users. Twitter's tweets or messages are limited to 140 characters. Because of this limitation, it is more difficult to express sentiment and the classification of the tweets is difficult as well. Sentiment analysis can be done on two types: emotion and opinion. This research completely focuses on sentiment analysis of opinions. These opinions can be divided in three different classes: positive, negative and neutral ( somewhere between positive and negative). The main goal of this study is to build a model that predicts election movement and provide sentiment score from Twitter messages (which can not exceed 140 characters). In this project, the author applies a novel approach that classifies sentiment and emotions of Twitter tweets automatically in positive, negative or neutral classes. For the sentiment, first of all, tweets from twitter were retrieved and converted into the dataset. After pre-processing the data the proposed algorithm named TWEELYZER was applied to the dataset. At the end, the performance of TWEELYZER was measured in terms of accuracy and recall. In this project, all tweets of people regarding to movies, brands, actors and actresses were collected from twitter and then cleaned and analysed according to the proposed algorithm. These tweets were collected using R Studio software. Several processes took place in pre-processing the tweets. After pre-processing the data, using R Studio led to several insights.
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