SENTIMENT ANALYSIS FOR USER REVIEWS ON SOCIAL MEDIA.

2017 ◽  
Vol 4 (02) ◽  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
User Reviews

USING OF LSTM NETWORKS IN SENTIMENT ANALYSIS OF DOCUMENTS IN KAZAKH LANGUAGE

2020 ◽  
Vol 69 (1) ◽  
pp. 366-370
Author(s):  
N.K. Kadyrbek ◽  
◽  
М.Е. Mansurova ◽  
М.Е. Kyrgyzbayeva ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Mobile Phones ◽  
User Reviews ◽  
Average Value ◽  
First Case ◽  
The Neural Network

Due to the growing trust in information in social media resources, interest in the field of sentiment analysis is growing. Because sentiment analysis is one of the main technologies for monitoring the opinions of millions of users of social networks. The article discusses the use of LSTM networks in the analysis of the tonality of texts in the Kazakh language. For training the neural network, 1000 user reviews of mobile phones were used. The experiments were carried out in two ways: in the first case, preprocessing of the analyzed reviews was carried out, in the second case, the preprocessing was not carried out. The average value of the metric for assessing the quality of the pre-processed model reached 80%. This indicator is 11% higher than for a model trained on data without preprocessing. The results of the study allowed us to conclude that the preprocessing of the texts improves the quality of the model.

scholarly journals A Training-Optimization-Based Method for Constructing Domain-Specific Sentiment Lexicon

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Maokang Du ◽  
Xiaoguang Li ◽  
Longyan Luo
Keyword(s):  
Social Media ◽  
Genetic Algorithms ◽  
Sentiment Analysis ◽  
Linguistic Knowledge ◽  
High Quality ◽  
Specific Domain ◽  
User Reviews ◽  
Domain Specific ◽  
Linguistic Rules ◽  
Sentiment Lexicon

Sentiment analysis has been widely used in text mining of social media to discover valuable information from user reviews. Sentiment lexicon is an essential tool for sentiment analysis. Recent research studies indicate that constructing sentiment lexicons for special domains can achieve better results in sentiment analysis. However, it is not easy to construct a sentiment lexicon for a specific domain because most current methods highly depend on general sentiment lexicons and complex linguistic rules. In this paper, the construction of sentiment lexicon is transformed into a training-optimization process. In our scheme, the accuracy of sentiment classification is used as the optimization objective. The candidate sentiment lexicons are regarded as the individuals that need to be optimized. Then, two genetic algorithms are specially designed to adjust the values of sentiment words in lexicon. Finally, the best individual evolved in the presented genetic algorithms is selected as the sentiment lexicon. Our method only depends on some labelled texts and does not need any linguistic knowledge or prior knowledge. It provides a simple and easy way to construct a sentiment lexicon in a specific domain. Experiment results show that the proposed method has good flexibility and can generate high-quality sentiment lexicon in specific domains.

scholarly journals Using Social Media in Tourist Sentiment Analysis: A Case Study of Andalusia during the Covid-19 Pandemic

2021 ◽  
Vol 13 (7) ◽  
pp. 3836
Author(s):  
David Flores-Ruiz ◽  
Adolfo Elizondo-Salto ◽  
María de la O. Barroso-González
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Cost Savings ◽  
Mass Scale ◽  
Tourism Sector ◽  
The Social ◽  
Analytical Tools ◽  
Media Data

This paper explores the role of social media in tourist sentiment analysis. To do this, it describes previous studies that have carried out tourist sentiment analysis using social media data, before analyzing changes in tourists’ sentiments and behaviors during the COVID-19 pandemic. In the case study, which focuses on Andalusia, the changes experienced by the tourism sector in the southern Spanish region as a result of the COVID-19 pandemic are assessed using the Andalusian Tourism Situation Survey (ECTA). This information is then compared with data obtained from a sentiment analysis based on the social network Twitter. On the basis of this comparative analysis, the paper concludes that it is possible to identify and classify tourists’ perceptions using sentiment analysis on a mass scale with the help of statistical software (RStudio and Knime). The sentiment analysis using Twitter data correlates with and is supplemented by information from the ECTA survey, with both analyses showing that tourists placed greater value on safety and preferred to travel individually to nearby, less crowded destinations since the pandemic began. Of the two analytical tools, sentiment analysis can be carried out on social media on a continuous basis and offers cost savings.

scholarly journals Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews

2021 ◽  
Author(s):  
Asad Khattak ◽  
Muhammad Zubair Asghar ◽  
Zain Ishaq ◽  
Waqas Haider Bangyal ◽  
Ibrahim A Hameed
Keyword(s):  
Sentiment Analysis ◽  
User Reviews ◽  
Analysis System

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

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