Sentiment Analysis - Tweets Related to President Trump

Sharif, Malak
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Al-Quds University, Deanship of Scientific Research
In our project, tweets are collected using the Twitter streaming API from Twitter. The collected tweets are pre-processed using PHP libraries language. The features of the tweets are selected based on Naïve Bayes classifier and are used to classify the tweets as positive, negative or natural. A Sentimental analysis using Twitter data was performed on the opinions about President Trump. A total of 120,000 tweets were collected for analysis in different languages. English tweets were only used. After removing duplicates, retweets, and the cleaning steps, only 21,232 tweets were used in the analyses. Upon sentiment analysis of retrieved Tweets, tweets carried more neutral sentiments about President Trump. About 58% were neutral sentiments, and 25% was positive, and the least were negative 17%. The research has taken President Trump as a target 'case study' of this project, but measures can be applied to other goals in flexibility, only by changing the target element. This project makes it easier for the user to obtain the summarized report about the opinion of Twitter. It is also used to support them in the decision-making process in their daily life activities.