JOURNAL ARTICLE

Sentiment Analysis from Tweets using Convolutional Neural Networks

Abstract

By current improvements of web technology nowadays, usage of social media has increased. Twitter is a web site where millions share their opinions. Political parties, firms and other establishments has been examining data at these social media sites to learn person’s opinions about themselves. Reporting the sharing of millions of persons instantly is done more easily by using machine and deep learning techniques. In this work, sentiment analysis is done by the Convolutional Neural Network which has wide-spread usage in deep learning. Besides other known works, improvements in feature selection have been applied in order to meet higher success rate. Model has been trained by the different data sets and tested in other data sets. The model has reached to 97% success rate by the training data. 90% and 89% success rates have been achieved on the tests applied to other data sets.

Keywords:
Convolutional neural network Computer science Social media Sentiment analysis Feature selection Deep learning Artificial intelligence Selection (genetic algorithm) Order (exchange) Data science Machine learning Artificial neural network Web traffic Feature (linguistics) World Wide Web The Internet

Metrics

1
Cited By
0.14
FWCI (Field Weighted Citation Impact)
28
Refs
0.53
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Sentiment Analysis and Opinion Mining
Physical Sciences →  Computer Science →  Artificial Intelligence
Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence
Traffic Prediction and Management Techniques
Physical Sciences →  Engineering →  Building and Construction
© 2026 ScienceGate Book Chapters — All rights reserved.