Abstract
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.