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Sentiment Analysis in Social Media Data

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A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".

Deadline for manuscript submissions: closed (18 December 2023) | Viewed by 18453

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Dr. Hiram Calvo

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Guest Editor

Centro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México 07738, Mexico
Interests: computational linguistics; text processing; lexical semantics

Special Issue Information

Dear Colleagues,

The development of the Internet and its rapid popularity have made it one of our main tools for consulting and disseminating information. With the development and exponential growth of social networks, the way in which human beings relate to each other has been affected since interaction through these has become a daily task, to such an extent that we can even maintain interpersonal relationships exclusively online with the use of certain platforms. In addition, the recent COVID-19 pandemic caused an accelerated evolution of this phenomenon because we were forced to interact remotely. The use of social networks has become so popular that, according to the publication "Digital 2021"¹, approximately 57% of the world population actively uses social networks such as Twitter, and on average, we invest around 2h 27m daily into this activity. Online platforms have quickly become involved in public discourse, their algorithms helping citizens join social groups, sort through the noise of public discourse, and even keep abreast of current events.

Posts on social networks can be on any topic, and furthermore, there are few restrictions on the content of the posts (e.g., news, comments, etc.). The content of the comments is usually charged with the emotions of the person who publishes them. This emotional charge is useful for identifying the points of view of the users. Social networks give us the opportunity to understand how readers react to a variety of topics, from politics to entertainment. Some of these topics can be controversial if people debate the topic for a period of time. This Special Issue is devoted to recent research in sentiment analysis in social networks, focusing both on the creation of new resources and their applications, as well as algorithms for finding interesting patterns and social groups within them.

_______________________________

¹ https://datareportal.com/reports/digital-2021-october-global-statshot

Dr. Hiram Calvo
Guest Editor

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Keywords

social groups discovery
sentiment analysis
emotional reactions to posts
emotion models applications to social networks
diachronic sentiment analysis

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Published Papers (5 papers)

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19 pages, 958 KiB

Open AccessArticle

Multimodal Hinglish Tweet Dataset for Deep Pragmatic Analysis

by Pratibha, Amandeep Kaur, Meenu Khurana and Robertas Damaševičius

Data 2024, 9(2), 38; https://doi.org/10.3390/data9020038 - 15 Feb 2024

Cited by 6 | Viewed by 2647

Abstract

Wars, conflicts, and peace efforts have become inherent characteristics of regions, and understanding the prevailing sentiments related to these issues is crucial for finding long-lasting solutions. Twitter/‘X’, with its vast user base and real-time nature, provides a valuable source to assess the raw emotions and opinions of people regarding war, conflict, and peace. This paper focuses on collecting and curating hinglish tweets specifically related to wars, conflicts, and associated taxonomy. The creation of said dataset addresses the existing gap in contemporary literature, which lacks comprehensive datasets capturing the emotions and sentiments expressed by individuals regarding wars, conflicts, and peace efforts. This dataset holds significant value and application in deep pragmatic analysis as it enables future researchers to identify the flow of sentiments, analyze the information architecture surrounding war, conflict, and peace effects, and delve into the associated psychology in this context. To ensure the dataset’s quality and relevance, a meticulous selection process was employed, resulting in the inclusion of explanable 500 carefully chosen search filters. The dataset currently has 10,040 tweets that have been validated with the help of human expert to make sure they are correct and accurate. Full article

(This article belongs to the Special Issue Sentiment Analysis in Social Media Data)

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27 pages, 6888 KiB

Open AccessArticle

Public Perception of ChatGPT and Transfer Learning for Tweets Sentiment Analysis Using Wolfram Mathematica

by Yankang Su and Zbigniew J. Kabala

Data 2023, 8(12), 180; https://doi.org/10.3390/data8120180 - 28 Nov 2023

Cited by 8 | Viewed by 3854

Abstract

Understanding public opinion on ChatGPT is crucial for recognizing its strengths and areas of concern. By utilizing natural language processing (NLP), this study delves into tweets regarding ChatGPT to determine temporal patterns, content features, and topic modeling and perform a sentiment analysis. Analyzing a dataset of 500,000 tweets, our research shifts from conventional data science tools like Python and R to exploit Wolfram Mathematica’s robust capabilities. Additionally, with the aim of solving the problem of ignoring semantic information in the LDA model feature extraction, a synergistic methodology entwining LDA, GloVe embeddings, and K-Nearest Neighbors (KNN) clustering is proposed to categorize topics within ChatGPT-related tweets. This comprehensive strategy ensures semantic, syntactic, and topical congruence within classified groups by utilizing the strengths of probabilistic modeling, semantic embeddings, and similarity-based clustering. While built-in sentiment classifiers often fall short in accuracy, we introduce four transfer learning techniques from the Wolfram Neural Net Repository to address this gap. Two of these techniques involve transferring static word embeddings, “GloVe” and “ConceptNet”, which are further processed using an LSTM layer. The remaining techniques center on fine-tuning pre-trained models using scantily annotated data; one refines embeddings from language models (ELMo), while the other fine-tunes bidirectional encoder representations from transformers (BERT). Our experiments on the dataset underscore the effectiveness of the four methods for the sentiment analysis of tweets. This investigation augments our comprehension of user sentiment towards ChatGPT and emphasizes the continued significance of exploration in this domain. Furthermore, this work serves as a pivotal reference for scholars who are accustomed to using Wolfram Mathematica in other research domains, aiding their efforts in text analytics on social media platforms. Full article

(This article belongs to the Special Issue Sentiment Analysis in Social Media Data)

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19 pages, 5497 KiB

Open AccessArticle

ChatGPT across Arabic Twitter: A Study of Topics, Sentiments, and Sarcasm

by Shahad Al-Khalifa, Fatima Alhumaidhi, Hind Alotaibi and Hend S. Al-Khalifa

Data 2023, 8(11), 171; https://doi.org/10.3390/data8110171 - 14 Nov 2023

Cited by 2 | Viewed by 4668

Abstract

While ChatGPT has gained global significance and widespread adoption, its exploration within specific cultural contexts, particularly within the Arab world, remains relatively limited. This study investigates the discussions among early Arab users in Arabic tweets related to ChatGPT, focusing on topics, sentiments, and the presence of sarcasm. Data analysis and topic-modeling techniques were employed to examine 34,760 Arabic tweets collected using specific keywords. This study revealed a strong interest within the Arabic-speaking community in ChatGPT technology, with prevalent discussions spanning various topics, including controversies, regional relevance, fake content, and sector-specific dialogues. Despite the enthusiasm, concerns regarding ethical risks and negative implications of ChatGPT’s emergence were highlighted, indicating apprehension toward advanced artificial intelligence (AI) technology in language generation. Region-specific discussions underscored the diverse adoption of AI applications and ChatGPT technology. Sentiment analysis of the tweets demonstrated a predominantly neutral sentiment distribution (92.8%), suggesting a focus on objectivity and factuality over emotional expression. The prevalence of neutral sentiments indicated a preference for evidence-based reasoning and logical arguments, fostering constructive discussions influenced by cultural norms. Sarcasm was found in 4% of the tweets, distributed across various topics but not dominating the conversation. This study’s implications include the need for AI developers to address ethical concerns and the importance of educating users about the technology’s ethical considerations and risks. Policymakers should consider the regional relevance and potential scams, emphasizing the necessity for ethical guidelines and regulations. Full article

(This article belongs to the Special Issue Sentiment Analysis in Social Media Data)

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17 pages, 1617 KiB

Open AccessArticle

Analysis of Government Policy Sentiment Regarding Vacation during the COVID-19 Pandemic Using the Bidirectional Encoder Representation from Transformers (BERT)

by Intan Nurma Yulita, Victor Wijaya, Rudi Rosadi, Indra Sarathan, Yusa Djuyandi and Anton Satria Prabuwono

Data 2023, 8(3), 46; https://doi.org/10.3390/data8030046 - 23 Feb 2023

Cited by 4 | Viewed by 3610

Abstract

To address the COVID-19 situation in Indonesia, the Indonesian government has adopted a number of policies. One of them is a vacation-related policy. Government measures with regard to this vacation policy have produced a wide range of viewpoints in society, which have been extensively shared on social media, including YouTube. However, there has not been any computerized system developed to date that can assess people’s social media reactions. Therefore, this paper provides a sentiment analysis application to this government policy by employing a bidirectional encoder representation from transformers (BERT) approach. The study method began with data collecting, data labeling, data preprocessing, BERT model training, and model evaluation. This study created a new dataset for this topic. The data were collected from the comments section of YouTube, and were categorized into three categories: positive, neutral, and negative. This research yielded an F-score of 84.33%. Another contribution from this study regards the methodology for processing sentiment analysis in Indonesian. In addition, the model was created as an application using the Python programming language and the Flask framework. The government can learn the extent to which the public accepts the policies that have been implemented by utilizing this research. Full article

(This article belongs to the Special Issue Sentiment Analysis in Social Media Data)

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13 pages, 3390 KiB

Open AccessData Descriptor

Sentiment Analysis of Multilingual Dataset of Bahraini Dialects, Arabic, and English

by Thuraya Omran, Baraa Sharef, Crina Grosan and Yongmin Li

Data 2023, 8(4), 68; https://doi.org/10.3390/data8040068 - 30 Mar 2023

Cited by 1 | Viewed by 2299

Abstract

Sentiment analysis is an application of natural language processing (NLP) that requires a machine learning algorithm and a dataset. In some cases, the dataset availability is scarce, particularly with Arabic dialects, precisely the Bahraini ones, which necessitates using an approach such as translation, where a rich source language is exploited to create the target language dataset. In this study, a dataset of Amazon product reviews in Bahraini dialects is presented. This dataset was generated using two cascading stages of translation—a machine translation followed by a manual one. Machine translation was applied using Google Translate to translate English Amazon product reviews into Standard Arabic. In contrast, the manual approach was applied to translate the resulting Arabic reviews into Bahraini ones by qualified native speakers utilizing constructed customized forms. The resulting parallel dataset of English, Standard Arabic, and Bahraini dialects is called English_Modern Standard Arabic_Bahraini Dialects product reviews for sentiment analysis “E_MSA_BDs-PR-SA”. The dataset is balanced, composed of 2500 positive and 2500 negative reviews. The sentiment analysis process was implemented using a stacked LSTM deep learning model. The Bahraini dialect product dataset can be utilized in the transfer learning process for sentimentally analyzing another dataset in Bahraini dialects. Full article

(This article belongs to the Special Issue Sentiment Analysis in Social Media Data)

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