Analyzing sentiment system to specify polarity by lexicon-based

Hamed, Dhafar; Abbas, Ayad R.; Sadiq, Ahmed T.

doi:10.11591/eei.v10i1.2471

Cited by 24 publications

(11 citation statements)

References 20 publications

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“…Abd et al [ 80 ] further aimed to recognise the emotional segmentation of a movie reviewer based on the entertainment domain by using this approach to extract sentiments from a given text and classify them. Lexicon based approach helps them achieve a significant result by identifying the contextual polarity for a large subset of sentiment.…”

Section: Discussionmentioning

confidence: 99%

Opinion mining for national security: techniques, domain applications, challenges and research opportunities

et al. 2021

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Background Opinion mining, or sentiment analysis, is a field in Natural Language Processing (NLP). It extracts people’s thoughts, including assessments, attitudes, and emotions toward individuals, topics, and events. The task is technically challenging but incredibly useful. With the explosive growth of the digital platform in cyberspace, such as blogs and social networks, individuals and organisations are increasingly utilising public opinion for their decision-making. In recent years, significant research concerning mining people’s sentiments based on text in cyberspace using opinion mining has been explored. Researchers have applied numerous opinions mining techniques, including machine learning and lexicon-based approach to analyse and classify people’s sentiments based on a text and discuss the existing gap. Thus, it creates a research opportunity for other researchers to investigate and propose improved methods and new domain applications to fill the gap. Methods In this paper, a structured literature review has been done by considering 122 articles to examine all relevant research accomplished in the field of opinion mining application and the suggested Kansei approach to solve the challenges that occur in mining sentiments based on text in cyberspace. Five different platforms database were systematically searched between 2015 and 2021: ACM (Association for Computing Machinery), IEEE (Advancing Technology for Humanity), SCIENCE DIRECT, SpringerLink, and SCOPUS. Results This study analyses various techniques of opinion mining as well as the Kansei approach that will help to enhance techniques in mining people’s sentiment and emotion in cyberspace. Most of the study addressed methods including machine learning, lexicon-based approach, hybrid approach, and Kansei approach in mining the sentiment and emotion based on text. The possible societal impacts of the current opinion mining technique, including machine learning and the Kansei approach, along with major trends and challenges, are highlighted. Conclusion Various applications of opinion mining techniques in mining people’s sentiment and emotion according to the objective of the research, used method, dataset, summarized in this study. This study serves as a theoretical analysis of the opinion mining method complemented by the Kansei approach in classifying people’s sentiments based on text in cyberspace. Kansei approach can measure people’s impressions using artefacts based on senses including sight, feeling and cognition reported precise results for the assessment of human emotion. Therefore, this research suggests that the Kansei approach should be a complementary factor including in the development of a dictionary focusing on emotion in the national security domain. Also, this theoretical analysis will act as a reference to researchers regarding the Kansei approach as one of the techniques to improve hybrid approaches in opinion mining.

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Section: Discussionmentioning

confidence: 99%

Opinion mining for national security: techniques, domain applications, challenges and research opportunities

et al. 2021

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“…The cleanup process includes removing unique hypertext markup language (HTML) entities, converting all characters in words to lowercase, removing hyperlinks, punctuation, whitespace, and special characters that appear in sentences e.g., "([0-9] +) | (#) | (@[A-Za-z0-9] +) | ([^0-9A-Za-z \t]) | (\w +: \ / \ / \ S +) " or English abbreviation e.g., don't = not or I'm = I am or OMG = Oh My God. Data cleansing is a very necessary step to initialize data for the next steps of preprocessing [10]- [13].…”

Section: Data Cleaningmentioning

confidence: 99%

Naïve-Bayes family for sentiment analysis during COVID-19 pandemic and classification tweets

Ressan

Hassan

2022

IJEECS

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This paper proposes a system to analyze the sentiments of tweeters. It is to build an accurate model to detect different emotions in a tweet. The analysis takes place through several stages (i.e., pre-processing, feature extraction, and training more than one machine learning (ML)). Naïve Bayes, Multinomial Naïve Bayes and Bernoulli Naïve Bayes were selected as supervised machine learning for sentiment analysis using a dataset of 3,057 tweets with users ranging from fear to happiness, anger, and sadness because this method is suitable for solving a problem of this type. This system was also applied to another dataset of 10,000 Tweets (5,000 positive and 5,000 negatives). This approach, consisting of three Naïve Bayes classification models, was applied to two datasets to analyze the sentiment used in them and classify each category separately. The Multinomial Naïve Bayes model outperformed the other models Where it achieved an accuracy of (91.6%) when applied to the first dataset and accuracy (87.6%) when applied to the second dataset. The researchers aim to continue this research with larger data by using other methods of sentiment analysis to predict users' thoughts about COVID-19 or any other problem and to obtain higher accuracy for the models used.

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“…Several researches and different processes are performed in the field of sentiment analysis. Authors in [14], [15] used unsupervised learning algorithms to calculate the average semantic orientation of texts. In the article of [16], authors defined the feature of a product based on latent semantic analysis (LSA).…”

Section: Related Workmentioning

confidence: 99%

Sentiment analysis of Twitter posts related to the COVID-19 vaccines

Alabid

Katheeth

2021

IJEECS

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A real threat to the people of the world has appeared as a result of the spread of the Coronavirus disease of 2019 (COVID-19) disease. A lot of scientific and financial support has been made to devote vaccines capable of ending this epidemic. However, these vaccines have become a subject of debate between individuals, as some people tend to support taking vaccines and others rejecting them. This paper aims to create a framework model to classify the sentiment and opinions of individuals that published in Twitter regarding the COVID-19 vaccines. Identify those opinions can help public health institutions to know public opinions and direct their efforts towards promoting taking vaccinations. Two of the machines learning classification models which are the support vector machine (SVM) and naive Bayes (NB) classifier are applied here. Other pre-processing methods were applied as well to filter unstructured tweets.

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Analyzing sentiment system to specify polarity by lexicon-based

Cited by 24 publications

References 20 publications

Opinion mining for national security: techniques, domain applications, challenges and research opportunities

Opinion mining for national security: techniques, domain applications, challenges and research opportunities

Naïve-Bayes family for sentiment analysis during COVID-19 pandemic and classification tweets

Sentiment analysis of Twitter posts related to the COVID-19 vaccines

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