Authors :
Faizan.
Volume/Issue :
Volume 4 - 2019, Issue 2 - February
Google Scholar :
https://goo.gl/DF9R4u
Scribd :
https://goo.gl/Q4Dtwc
Thomson Reuters ResearcherID :
https://goo.gl/KTXLC3
Abstract :
With the evolving behavior of different types of social networking sites like Instagram, twitter, snapchat etc , the data posted by people i.e the users of a particular social site is increasing drastically . So much so that almost millions and billions of data may it be textual, video or audio is posted per day. This is because there are millions of users of a particular site. These users intend to share their thoughts, views related to any topic of their choosing. Some of these users even post in vain. These posts are short hence only meant to express a particular view of a particular user regarding a particular thing. In this paper we aim to derive the feelings behind these posts. For this we have chosen twitter as a social networking site. The posts in this social networking site are known as tweets. In this paper we scrutinize methods of preprocessing and extraction of twitter data using python and then train as well as test this data against a classifier in order to derive the sentiments behind tweets.
With the evolving behavior of different types of social networking sites like Instagram, twitter, snapchat etc , the data posted by people i.e the users of a particular social site is increasing drastically . So much so that almost millions and billions of data may it be textual, video or audio is posted per day. This is because there are millions of users of a particular site. These users intend to share their thoughts, views related to any topic of their choosing. Some of these users even post in vain. These posts are short hence only meant to express a particular view of a particular user regarding a particular thing. In this paper we aim to derive the feelings behind these posts. For this we have chosen twitter as a social networking site. The posts in this social networking site are known as tweets. In this paper we scrutinize methods of preprocessing and extraction of twitter data using python and then train as well as test this data against a classifier in order to derive the sentiments behind tweets.
