Advanced International Journal for Research

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Text-Based Sentiment Analysis

Author(s) Akash Sharma
Country India
Abstract In this fast-paced world where life is becoming increasingly complex day by day, it has become significantly more difficult to keep up with the latest trends and developments. Sentiment Analysis can prove to be beneficial in making decisions by providing valuable insights about how people feel regarding different subjects. Sentiment Analysis refers to monitoring human emotions which targets mapping people’s opinion regarding a specific subject. These days Social Networking sites have provided the public an open platform to share their opinions, thoughts and feelings on a wide range of topics and this has resulted in the availability of huge amount of data that can be analyzed to understand the public sentiment. Text Based Sentiment Analysis is the evaluation of human emotions based on the text gathered from various sources.
Now businesses have started recognizing the importance of providing a good user experience to their clients. Sentiment Analysis can help businesses to understand how customers feel about their products and services. This very data can be utilized to improve the customer experience.
In recent years, it has become significantly important. It helps understanding the motion of the mass by evaluating the accumulated data from various resources with the utilization of the Deep Learning Algorithms. We aim to develop a sentiment analysis model for tracking public opinion on a variety of topics. Data used in this study is related to the posts on social media platforms predominantly the comments from YouTube videos. We look forward to evaluate the text gathered from the comments and understand the emotional tone, public opinion and feedback by collecting and reprocessing the data, applying algorithms to perform text analysis of human language.

Such models when deployed can prove to be very helpful in many factors and have a variety of applications. Domains like “public opinion on political issues”, “Brand promotions and mentions”, “improving customer service”, etc. are the main areas
Keywords Sentiment Analysis, Deep Learning, NLP, detection, CNN
Field Engineering
Published In Volume 1, Issue 1, November-December 2020
Published On 2020-12-08
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E-ISSN 3048-7641
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