Research on the Sentiment Analysis Based on Machine Learning and Feature Extraction Algorithm

Jin, Xiaofang; Xu, Ying

doi:10.1109/icsess47205.2019.9040804

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…Term frequency-inverse document frequency (TF-IDF) is an NLP technique for text feature extraction that is more functional and widely accepted than other known methods. 27 It calculates TF-IDF sparse matrix and discovers the underlying logic of words in the text. Furthermore, it evaluates importance of a word to a document in a corpus.…”

Section: Term Frequency-inverse Document Frequency Techniquementioning

confidence: 99%

Predicting the language of depression from multivariate twitter data using a feature‐rich hybrid deep learning model

Kour

Gupta

2022

Concurrency and Computation

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Depression is a clinical entity that might be difficult for a psychiatrist to diagnose it effectively on time. A depressed person usually suffers from distress and anxiety, leading to serious consequences if not diagnosed early. Social media platforms facilitate users to exchange ideas and dialogs, resulting in the collection of a huge volume of data. Analyzing user's online behavior to categorize depression is a challenging task for researchers. This motivated researchers to investigate machine learning, deep learning, and natural language processing techniques supporting research related to depression prediction. The dataset used in the study is a large-scale Twitter dataset. This article aims to investigate a hybrid CNN-LSTM deep learning model with the Word2Vec feature extraction technique for classifying depressive sentiments from Twitter data. By using TF-IDF, PCA, and Word2Vec approaches, this model utilizes significant linguistic features present within the text. The proposed model is evaluated on four benchmark datasets and its efficiency is compared with four traditional machine learning models. Moreover, the proposed model's performance is compared to three deep learning-based hybrid models. The proposed model showed comparable performance with the hybrid deep learning-based models and outperformed state-of-the-art machine learning techniques with an accuracy of 96.78% and an MSE score of 3.21.

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Section: Term Frequency-inverse Document Frequency Techniquementioning

confidence: 99%

Predicting the language of depression from multivariate twitter data using a feature‐rich hybrid deep learning model

Kour

Gupta

2022

Concurrency and Computation

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RFLSem: A Lightweight Model for Textual Sentiment Analysis

Yang

Dang

Liu

et al. 2023

Lecture Notes in Computer Science

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Specialists, Scientists, and Sentiments: Word2Vec and Doc2Vec in Analysis of Scientific and Medical Texts

Chen

Sokolova

2021

SN COMPUT. SCI.

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Analyze performance of unsupervised embedding algorithms in sentiment analysis of knowledge-rich data sets. We apply state-of-the-art embedding algorithms Word2Vec and Doc2Vec as the learning techniques. The algorithms build word and document embeddings in an unsupervised manner. To assess the algorithms’ performance, we define sentiment metrics and use a semantic lexicon SentiWordNet (SWN) to establish the benchmark measures. Our empirical results are obtained on the Obesity data set from i2b2 clinical discharge summaries and the Reuters Science dataset. We use the Welch’s test to analyze the obtained sentiment evaluation. On the Obesity data, the Welch’s test found significant difference between the SWN evaluation of the most positive and most negative texts. On the same data, the Word2Vec results support the SWN results, whereas the Doc2Vec results partially correspond to the Word2Vec and the SWN results. On the Reuters data, the Welch’s test did not find significant difference between the SWN evaluation of the most positive and most negative texts. On the same data, Word2Vec and Doc2Vec results only in part correspond to the SWN results. In unsupervised sentiment analysis of medical and scientific texts, the Word2Vec sentiment analysis has been more consistent with the SentiWordNet sentiment assessment than the Doc2Vec sentiment analysis. The Welch’s test of the SentiWordNet results has been a strong indicator of future correspondence between Word2Vec and SentiWordNet results.

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Research on the Sentiment Analysis Based on Machine Learning and Feature Extraction Algorithm

Cited by 5 publications

References 7 publications

Predicting the language of depression from multivariate twitter data using a feature‐rich hybrid deep learning model

Predicting the language of depression from multivariate twitter data using a feature‐rich hybrid deep learning model

RFLSem: A Lightweight Model for Textual Sentiment Analysis

Specialists, Scientists, and Sentiments: Word2Vec and Doc2Vec in Analysis of Scientific and Medical Texts

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