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An Analysis of Sentimental Data using Machine Learning Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 166 - Number 3 |
| Year of Publication: 2017 |
| Authors: Surbhi Chandhok, Romil Anand, Soumay Gupta, Aatif Jamshed |
10.5120/ijca2017913955
|
Surbhi Chandhok, Romil Anand, Soumay Gupta, Aatif Jamshed . An Analysis of Sentimental Data using Machine Learning Techniques. International Journal of Computer Applications. 166, 3 ( May 2017), 35-39. DOI=10.5120/ijca2017913955
Abstract
Sentiment analysis, basically, comprises of identifying different opinions or emotions in source text and then classifying them in its accordance. Social media generates extensive sentiment rich dataset in the form of Social media in through status updates, tweets, short video clips, blog posts etc. At the same time, it is relatively more difficult to do a Twitter estimation investigation when contrasted with general assessment examination. This is because of the presence of foul words, slang language and incorrect spellings. The maximum character limit per tweet in Twitter is 140. There are two strategies used to analyse sentiments, emotions and/or opinions from the source file. These two strategies are: Approach based on knowledge Approach based on learning of machine. Throughout this paper, we’ve tried to analyse the posts of twitter related to a wide range of electronic products such as mobiles, laptops, video games television sets etc. using Machine Learning approach. During the time spent doing notion investigation in a specific area, it is conceivable to distinguish and make sense of the impact of space data as per the estimation order. We've additionally presented another element vector, in order to just group the tweets as plain constructive, pessimistic and infer people groups' feeling about items.
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