Investigation of Different Machine Learning Approaches for Analyzing Human Attitude

Abstract

Sentiment analysis is widely used in data science, where data is used and analyzed, which is available in various social media and internet. Sentiment analysis is a qualitative processing of text data that extracts and defines subjective details in the source material and allows a company or something like that to recognize the feeling of its brand or products or services when tracking online conversations and feedback. Social media analysis is usually limited to simple analysis of sentiment or count based metrics. Everyone is articulate one way or another in in this time. Most social media and android apps like Facebook or WhatsApp or Twitter have a lot of information which is accessible in this highly developed and re imagined world. Twitter is one of the most common and international networks. Twitter is that kind of social media where many users can express their opinion and feelings through small tweets. These tweets can be analyzed using different machine learning algorithms. Twitter sentiment analysis is a very popular research work now. Most of the work is on two types of sentiment detection, it can be either positive or negative. This paper includes neutral sentiment. So, the proposed idea is to find out a tweet is positive or negative or neutral with a better accuracy. In this paper tweeter data has been encoded using label encoder OneHot encoder. After that applying different types of pre-processors, we have feed that numeric form to the machine learning classifier algorithms. Although our accuracy is quite low, in future we shall develop the algorithm for better accuracy.