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.

Generate domain-specific sentiment lexicon for review sentiment analysis

2018 ◽  
Vol 77 (16) ◽  
pp. 21265-21280 ◽  
Author(s):  
Hongyu Han ◽  
Jianpei Zhang ◽  
Jing Yang ◽  
Yiran Shen ◽  
Yongshi Zhang
Keyword(s):  
Sentiment Analysis ◽  
Domain Specific ◽  
Sentiment Lexicon

Sentiment search: an emerging trend on social media monitoring systems

2014 ◽  
Vol 66 (5) ◽  
pp. 553-580 ◽  
Author(s):  
Tung Thanh Nguyen ◽  
Tho Thanh Quan ◽  
Tuoi Thi Phan
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Real Data ◽  
Content Type ◽  
Social Monitoring ◽  
Cross Domain ◽  
Linguistic Rules ◽  
Domain Ontologies ◽  
Media Monitoring ◽  
Media Channels

Purpose – The purpose of this paper is to discuss sentiment search, which not only retrieves data related to submitted keywords but also identifies sentiment opinion implied in the retrieved data and the subject targeted by this opinion. Design/methodology/approach – The authors propose a retrieval framework known as Cross-Domain Sentiment Search (CSS), which combines the usage of domain ontologies with specific linguistic rules to handle sentiment terms in textual data. The CSS framework also supports incrementally enriching domain ontologies when applied in new domains. Findings – The authors found that domain ontologies are extremely helpful when CSS is applied in specific domains. In the meantime, the embedded linguistic rules make CSS achieve better performance as compared to data mining techniques. Research limitations/implications – The approach has been initially applied in a real social monitoring system of a professional IT company. Thus, it is proved to be able to handle real data acquired from social media channels such as electronic newspapers or social networks. Originality/value – The authors have placed aspect-based sentiment analysis in the context of semantic search and introduced the CSS framework for the whole sentiment search process. The formal definitions of Sentiment Ontology and aspect-based sentiment analysis are also presented. This distinguishes the work from other related works.

scholarly journals Polarity Classification Tool for Sentiment Analysis in Malay Language

2019 ◽  
Vol 8 (3) ◽  
pp. 259
Author(s):  
Normi Sham Awang Abu Bakar ◽  
Ros Aziehan Rahmat ◽  
Umar Faruq Othman
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
English Language ◽  
Sentiment Lexicon ◽  
The Social ◽  
Polarity Classification ◽  
Media Channels ◽  
Classification Tool

<p>The popularity of the social media channels has increased the interest among researchers in the sentiment analysis(SA) area. One aspect of the SA research is the determination of the polarity of the comments in the social media, i.e. positive, negative, and neutral. However, there is a scarcity of Malay sentiment analysis tools because most of the work in the literature discuss the polarity classification tool in English. This paper presents the development of a polarity classification tool called Malay Polarity Classification Tool(MaCT). This tool is developed based on the AFINN sentiment lexicon for English language. We have attempted to translate each word in AFINN to its Malay equivalent and later, use the lexicon to collect the sentiment data from Twitter. The Twitter data are then classified into positive, negative, and neutral. For the validation purpose, we collect 400 positive tweets, 400 negative tweets, and 200 neutral tweets, and later, run the tweets through our sentiment lexicon and found 90% score for precision, recall and accuracy. Our main contribution in the research is the new AFINN translation for Malay language and also the classification of the sentiment data.</p>

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 Lexicon-based sentiment analysis for stock movement prediction

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Zane Turner ◽  
◽  
Kevin Labille ◽  
Susan Gauch ◽  
◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Stock Price ◽  
Price Change ◽  
Probabilistic Approach ◽  
Conference Call ◽  
Domain Specific ◽  
Movement Prediction ◽  
Sentiment Lexicon ◽  
Textual Data ◽  
Sentiment Score

Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We present a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

scholarly journals Targeted Aspect-Based Sentiment Analysis for Lithuanian Social Media Reviews

2020 ◽  
Author(s):  
Mažvydas Petkevičius ◽  
Daiva Vitkutė-Adžgauskienė ◽  
Darius Amilevičius
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Word Order ◽  
System Architecture ◽  
Experimental Results ◽  
Specific Domain ◽  
Research Results

The paper presents research results for solving the task of targeted aspect-based sentiment analysis in the specific domain of Lithuanian social media reviews. Methodology, system architecture, relevant NLP tools and resources are described, finalized by experimental results showing that our solution is suitable for solving targeted aspect-based sentiment analysis tasks for under-resourced, morphologically rich and flexible word order languages.

scholarly journals Negation Handling in Machine Learning-Based Sentiment Classification for Colloquial Arabic

2020 ◽  
Vol 11 (4) ◽  
pp. 33-45
Author(s):  
Omar Alharbi
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Positive Impact ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Linguistic Knowledge ◽  
Sentiment Lexicon ◽  
Colloquial Arabic ◽  
Arabic Sentiment Analysis ◽  
The Impact

One crucial aspect of sentiment analysis is negation handling, where the occurrence of negation can flip the sentiment of a review and negatively affects the machine learning-based sentiment classification. The role of negation in Arabic sentiment analysis has been explored only to a limited extent, especially for colloquial Arabic. In this paper, the authors address the negation problem in colloquial Arabic sentiment classification using the machine learning approach. To this end, they propose a simple rule-based algorithm for handling the problem that affects the performance of a machine learning classifier. The rules were crafted based on observing many cases of negation, simple linguistic knowledge, and sentiment lexicon. They also examine the impact of the proposed algorithm on the performance of different machine learning algorithms. Furthermore, they compare the performance of the classifiers when their algorithm is used against three baselines. The experimental results show that there is a positive impact on the classifiers when the proposed algorithm is used compared to the baselines.

scholarly journals BengSentiLex and BengSwearLex: creating lexicons for sentiment analysis and profanity detection in low-resource Bengali language

2021 ◽  
Vol 7 ◽  
pp. e681
Author(s):  
Salim Sazzed
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Translation System ◽  
Low Resource ◽  
Part Of Speech ◽  
Sentiment Lexicon ◽  
Cross Lingual ◽  
Bengali Language

Bengali is a low-resource language that lacks tools and resources for various natural language processing (NLP) tasks, such as sentiment analysis or profanity identification. In Bengali, only the translated versions of English sentiment lexicons are available. Moreover, no dictionary exists for detecting profanity in Bengali social media text. This study introduces a Bengali sentiment lexicon, BengSentiLex, and a Bengali swear lexicon, BengSwearLex. For creating BengSentiLex, a cross-lingual methodology is proposed that utilizes a machine translation system, a review corpus, two English sentiment lexicons, pointwise mutual information (PMI), and supervised machine learning (ML) classifiers in various stages. A semi-automatic methodology is presented to develop BengSwearLex that leverages an obscene corpus, word embedding, and part-of-speech (POS) taggers. The performance of BengSentiLex compared with the translated English lexicons in three evaluation datasets. BengSentiLex achieves 5%–50% improvement over the translated lexicons. For identifying profanity, BengSwearLex achieves documentlevel coverage of around 85% in an document-level in the evaluation dataset. The experimental results imply that BengSentiLex and BengSwearLex are effective resources for classifying sentiment and identifying profanity in Bengali social media content, respectively.

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