Text-Based Sentimental Analysis to Understand User Experience Using Machine Learning Approaches

Title

Text-Based Sentimental Analysis to Understand User Experience Using Machine Learning Approaches

Creator

Gokulapriya R.; Sambandam R.K.; Balamurugan M.

Description

Data Analysis is turning into a driving force in every industry. It is a process in which data is analyzed in multiple ways to come to certain conclusions for the given situation. Sentiment analysis can be said to be a sub-section of data analysis where analysis is carried out on the emotions and opinions of the text. Social media has a plethora of sentiment data in various forms such as tweets, updates on the status, and so forth. Sentiment analysis on the huge volume of data can help in identifying the opinions of the general mass.The primary goal is to find the opinion of customers on the services of the Bangalore airport and to enhance the nature of these services according to the feedback provided. In this paper, we aim to measure customer opinion on services provided by Bangalore Airport through sentiment. Data is collected by a python-based scraper. The tweets are processed to determine whether they are of positive or negative opinion. These opinions are then analyzed to determine the factors which cause the negative opinions and the airport staff are alerted about the same. Various algorithms were used as part of the experimental analysis. LSTM produces more accuracy compared with existing approaches. 2023 IEEE.

Source

2023 9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023, pp. 402-408.

Date

2023-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Google Colab; LSTM; Python; Sentiment Analysis; Twitter

Coverage

Gokulapriya R., SOET, CHRIST (Deemed to be University), Dept. of Computer Science and Engineering, Bengaluru, India; Sambandam R.K., SOET, CHRIST (Deemed to be University), Dept. of Computer Science and Engineering, Bengaluru, India; Balamurugan M., SOET, CHRIST (Deemed to be University), Dept. of Computer Science and Engineering, Bengaluru, India

Rights

Restricted Access

Relation

ISBN: 979-835039737-6

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICACCS57279.2023.10112939

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