Studies On Development of Model for Sentiment Analysis with ML: A Review

• Author(s): Nagavelli Yogender Nath; Gattu Ramya; R. Prasanth Reddy; K. Mani Raju
• Paper ID: 1712456
• Page: 1155-1163
• Published Date: 31-12-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 6 December-2024

Abstract

Sentiment analysis refers to the automated process of identifying the underlying emotional tone conveyed in a text. This task has become increasingly crucial today due to the exponential growth of opinion-oriented text generation on various online platforms including social media and e-commerce websites. The process of sentiment analysis has evolved through different phases like the use of handcrafted rules, sentiment-oriented dictionaries, machine learning, deep learning, transformer models, and large language models. These different phases have enriched the field of sentiment analysis and enabled it to get better insights into text for sentiment analysis. One of the most impactful developments in sentiment analysis was the creation of embedding’s for words or sentences, which are useful for representing unstructured data as structured data. These embedding are representing words or sentences as numerical vectors in a high-dimensional space and have proven effective in capturing the semantic relationships between words. By leveraging the power of embedding’s, sentiment analysis models can understand the complexities of human language and provide more precise insights into people's emotions and opinions. However, word embedding’s not inherently generated for sentiment analysis, which means that they are not inherently sentiment-oriented. Therefore, many researchers have focused on developing sentiment-oriented word embedding’s for sentiment words, but they have overlooked the importance of intensity words such as “little”, “high”, and “extremely”. These intensity words determine the level of sentiment expressed in text, which is particularly useful for fine-level sentiment analysis. To address this issue, a review proposes an intensity-aware word embedding development model and sentiment analysis with ML.

Keywords

Sentiment analysis, Embedding, Machine learning methods, Hybrid methods

Citations

IRE Journals:
Nagavelli Yogender Nath, Gattu Ramya, R. Prasanth Reddy, K. Mani Raju "Studies On Development of Model for Sentiment Analysis with ML: A Review" Iconic Research And Engineering Journals Volume 8 Issue 6 2024 Page 1155-1163

IEEE:
Nagavelli Yogender Nath, Gattu Ramya, R. Prasanth Reddy, K. Mani Raju "Studies On Development of Model for Sentiment Analysis with ML: A Review" Iconic Research And Engineering Journals, 8(6)