The sentiment analysis model with multi-head self-attention and Tree-LSTM

2021 ◽  
Author(s):  
Lei Li ◽  
YiJian Pei ◽  
ChenYang Jin
Keyword(s):  
Sentiment Analysis ◽  
Analysis Model

An ANEW based Fuzzy Sentiment Analysis Model

2018 ◽  
Author(s):  
Andres Montoro ◽  
Jose A. Olivas ◽  
Arturo Peralta ◽  
Francisco P. Romero ◽  
Jesus Serrano-Guerrero
Keyword(s):  
Sentiment Analysis ◽  
Analysis Model

scholarly journals Sentiment analysis of MOOC reviews via ALBERT-BiLSTM model

2021 ◽  
Vol 336 ◽  
pp. 05008
Author(s):  
Cheng Wang ◽  
Sirui Huang ◽  
Ya Zhou
Keyword(s):  
Sentiment Analysis ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Analysis Model ◽  
Accuracy Rate ◽  
Massive Open Online ◽  
Data Set ◽  
Proposed Model ◽  
Contextual Feature

The accurate exploration of the sentiment information in comments for Massive Open Online Courses (MOOC) courses plays an important role in improving its curricular quality and promoting MOOC platform’s sustainable development. At present, most of the sentiment analyses of comments for MOOC courses are actually studies in the extensive sense, while relatively less attention is paid to such intensive issues as the polysemous word and the familiar word with an upgraded significance, which results in a low accuracy rate of the sentiment analysis model that is used to identify the genuine sentiment tendency of course comments. For this reason, this paper proposed an ALBERT-BiLSTM model for sentiment analysis of comments for MOOC courses. Firstly, ALBERT was used to dynamically generate word vectors. Secondly, the contextual feature vectors were obtained through BiLSTM pre-sequence and post-sequence, and the attention mechanism that could calculate the weight of different words in a sentence was applied together. Finally, the BiLSTM output vectors were input into Softmax for the classification of sentiments and prediction of the sentimental tendency. The experiment was performed based on the genuine data set of comments for MOOC courses. It was proved in the result that the proposed model was higher in accuracy rate than the already existing models.

scholarly journals Conceptual Sentiment Analysis Model

2018 ◽  
Vol 8 (4) ◽  
pp. 2358 ◽  
Author(s):  
Kranti Vithal Ghag ◽  
Ketan Shah
Keyword(s):  
Sentiment Analysis ◽  
Relative Frequency ◽  
Syntactic Structure ◽  
Research Work ◽  
Average Frequency ◽  
Semantic Structure ◽  
Analysis Model ◽  
Inverse Document Frequency ◽  
Document Frequency ◽  
Improve Accuracy

<span>Bag-of-words approach is popularly used for Sentiment analysis. It maps the terms in the reviews to term-document vectors and thus disrupts the syntactic structure of sentences in the reviews. Association among the terms or the semantic structure of sentences is also not preserved. This research work focuses on classifying the sentiments by considering the syntactic and semantic structure of the sentences in the review. To improve accuracy, sentiment classifiers based on relative frequency, average frequency and term frequency inverse document frequency were proposed. To handle terms with apostrophe, preprocessing techniques were extended. To focus on opinionated contents, subjectivity extraction was performed at phrase level. Experiments were performed on Pang &amp; Lees, Kaggle’s and UCI’s dataset. Classifiers were also evaluated on the UCI’s Product and Restaurant dataset. Sentiment Classification accuracy improved from 67.9% for a comparable term weighing technique, DeltaTFIDF, up to 77.2% for proposed classifiers. Inception of the proposed concept based approach, subjectivity extraction and extensions to preprocessing techniques, improved the accuracy to 93.9%.</span>

scholarly journals A Twitter Sentiment Analysis Model for Measuring Security and Educational Challenges: A Case Study in Saudi Arabia

2018 ◽  
Vol 14 (3) ◽  
pp. 360-367
Author(s):  
Hassan Abdullah Alqarni ◽  
Yahya AlMurtadha ◽  
Abdelrahman Osman Elfaki
Keyword(s):  
Saudi Arabia ◽  
Sentiment Analysis ◽  
Analysis Model ◽  
Educational Challenges

Sentiment Analysis Model Based on the Word Structural Representation

2021 ◽  
pp. 170-178
Author(s):  
Gulmira Bekmanova ◽  
Banu Yergesh ◽  
Altynbek Sharipbay
Keyword(s):  
Sentiment Analysis ◽  
Analysis Model ◽  
Structural Representation ◽  
Model Based
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