Sentiment Analysis on Social media
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Abstract
This paper suggested different improvement process to track and prevent shoplifting using technologies – Artificial Intelligence, Machine Learning, Data Analytics, and Process Automation. The detailed work is done in developing a system used for opinion analysis of a product or a service. The system readily processes the tweets by pulling data from twitter posts, pre-processing it and connecting it to Twitter API by REST call method and showing it graphically. We have given the analysis for the public tweets by API and filter them for various products, persons and services. For written product reviews, the best solution is a video review. Collecting comments from YouTube videos and extracting the exact tone or behaviour behind it. The most widely used approaches in opinion mining focus only on tweets or written product reviews available on websites like Amazon. Various emotions that can deal here namely Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, Curiosity, Excitement, Gratitude, Serenity, Hope, Pride, Amusement, Jealousy, Guilt, Discouragement, Frustration, Rejection, Disappointment, Loneliness, Interest, lack of interest, Concern, Sympathy and Calm. Online news is also now trending and extracting the proper tone behind the news. By applying machine learning algorithms like feature extraction, classification algorithm, natural language processing techniques, and more, polarity can be identified. The analysis is used to classify the sentiment as positive, negative, neutral, strongly positive, weakly positive, strong negative, and weak negative. The results have shown textually and graphically.
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References
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