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Aspect Based Sentiment Analysis - Twitter
  • Vashishtha Upadhyay ,
  • Jinan Fiaidhi
Lakehead University

Corresponding Author:[email protected]

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Vashishtha Upadhyay
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Jinan Fiaidhi
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Due to the increased use and popularity of social media platforms in the most recent technological period, sentiment categorization has emerged as an important research area among those platforms. When it comes to Twitter, the main problem of previous research is, they all did a sentiment classification on the document level (Tweet level). It cannot classify the sentiment for any particular aspect. When it comes to the review of any multifunctional product and service, gathering an overall positive or negative mood may not be helpful to the firms as it is more crucial to ascertain precisely what their customers are happy or upset about, to bring the updates and changes on that particular product and service.
In addition to this, what if someone wants to know the sentiments about recently generated data or tweets? What if someone wants to know the sentiment for data between a particular date range? What if users want to get sentiment of the tweets regarding current ongoing events and happenings? Along with this, very few of them performed aspect-based sentiment analysis on other platforms and they are using the same data set for training purposes as well as analytics purposes. So here we come up with the idea of Aspect based sentiment analysis on twitter, in which we train our model with a publicly available dataset, and then the user will give a particular hashtag and aspects. Our system will get tweets related to that specific hashtag from publicly available daily search twitter API and our model will take those tweets as input for analytics. Then machine learning operations will be performed on those tweets to find sentiment analysis for that hashtag’s tweet and its aspects with the best accuracy. In that way, we can get responses from people on any event, feedback or national issue, or matter of people’s support. The experimental findings also showed that our method beats current state-of-the-art approaches.