Comparative Study of Sentiment Analysis Techniques

Nandi, Abheek; Sharma, Preeti

doi:10.1201/9781003202240-72

Cited by 4 publications

(3 citation statements)

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“…Một số phương pháp máy học được sử dụng để phân loại các đánh giá: Naive Bayes 20 , Hồi quy Logistic 21 , Random Forest 22 , Support Vector Machine 23 và nhiều mô hình khác. Phương pháp máy học bắt đầu từ việc thu thập tập dữ liệu huấn luyện, trích xuất đặc trưng 24 và sau đó huấn luyện một bộ phân loại trên dữ liệu huấn luyện.. Cuối cùng, phân loại có giám sát cho biết cách thức tập dữ liệu thể hiện 25 . Các nghiên cứu trước đó đã đạt được những kết quả tốt trong lĩnh vực phân tích quan điểm, tuy nhiên vẫn còn những hạn chế như trao đổi trên.…”

Section: Các Phương Pháp Phân Tích Quan đIểmunclassified

Customer experience discovery model based on sentiment analysis and machine learning method

Nguyen,

Nguyen

et al. 2022

Sci. Tech.Dev. J. - Eco. Law Manag.

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Exploiting customer insights plays a key role in the long-term development strategy of every business, which strongly supports them in building trust with customers and enhancing competitive advantages. Especially in the period of rapid growth of technology, as there is a significant increase in online users, all the interactions and comments become more important in extracting valuable insights into customer behaviours and emotions. Our research aims to conduct sentiment analysis on comments in mobile commerce apps , then classify them into positive or negative sentiment, based on a hybrid approach that combines supervised machine learning methods and natural language processing techniques; at the same time, evaluating the model performance through the Confusion Matrix to ensure the reliability of the results. The study experimented on a proposed model with 04 machine learning methods including Naive Bayes, SVM, Random Forest and Logistic Regression on more than 935,000 comments collected from 04 popular mobile commerce apps in Vietnam (Tiki, Shopee, Lazada and Sendo). The experiment categorised positive and negative views with high accuracies of 91%, expressed by reports and charts that reflect customer trends and feelings. Moreover, the study also brings new and deeper perspectives on customer behaviours, assisting administrators to detect the strengths and weaknesses of services and apps, thereby improving user experience. Based on the research results, E-commerce businesses can analyse market trends and explore customers' needs and interests to develop effective product and service development strategies.

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Section: Các Phương Pháp Phân Tích Quan đIểmunclassified

Customer experience discovery model based on sentiment analysis and machine learning method

Nguyen,

Nguyen

et al. 2022

Sci. Tech.Dev. J. - Eco. Law Manag.

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“…Sentiment analysis, also called Opinion Mining, is one of the most popular techniques for the computational study of opinions and sentiments of people's thoughts, perceptions, and feelings expressed in the text [ 3 , 4 ]. Even though there are studies on stock market predictions [ 5 ] questioning whether the stock market can be predicted with the help of sentiment analysis or not, most works in this field have been carried out on the evaluation of consumer products and movie reviews [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers

Cam,

Demirel

et al. 2024

Heliyon

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“…Manual sentiment analysis necessitates increasing amounts of time and effort as the volume of data increases. One method that can be used to solve these problems is machine learning [5]. From the perspective of machine learning, sentiment analysis is a problem that requires supervised learning, i.e.…”

Section: Introductionmentioning

confidence: 99%

The hybrid of BERT and deep learning models for Indonesian sentiment analysis

Guna Mandhasiya,

Murfi,

Bustamam

2024

IJEECS

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<span>Artificial intelligence (AI) is one example of how data science innovation has advanced quickly in recent years and has greatly improved human existence. Neural networks, which are a type of machine learning model, are a fundamental component of deep learning in the field of AI. Deep learning models can carry out feature extraction and classification tasks in a single design because of their numerous neural network layers. Modern machine learning algorithms have been shown to perform worse than this model on tasks including text classification, audio recognition, imaginary, and pattern recognition. Deep learning models have outperformed AI-based methods in sentiment analysis and other text categorization tasks. Text data can originate from a number of places, including social media. Sentiment analysis is the computational examination of textual expressions of ideas and feelings. This study employs the convolutional neural network (CNN), long-short term memory (LSTM), CNN-LSTM, and LSTM-CNN models in a deep learning framework using bidirectional encoder representations from transformers (BERT) data representation to assess the performance of machine learning. The implementation of the model utilises YouTube discussion data pertaining to political films associated with the Indonesian presidential election of 2024. Confusion metrics, including as accuracy, precision, and recall, are then used to analyse the model’s performance.</span>

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Comparative Study of Sentiment Analysis Techniques

Cited by 4 publications

References 0 publications

Customer experience discovery model based on sentiment analysis and machine learning method

Customer experience discovery model based on sentiment analysis and machine learning method

Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers

The hybrid of BERT and deep learning models for Indonesian sentiment analysis

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