Polarity Classification Tool for Sentiment Analysis in Malay Language

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

Literature review on Real-time Location-Based Sentiment Analysis on Twitter

Sentiment analysis mainly supports sorting out the polarity and provides valuable information with the use of raw data in social media platforms. Many fields like health, business, and security require real-time data analysis for instant decision-making situations.Since Twitter is considered a popular social media platform to collect data easily, this paper is considering data analysis methods of Twitter data, real-time Twitter data analysis based on geo-location. Twitter data classification and analysis can be done with the use of diverse algorithms and deciding the most appropriate algorithm for data analysis, can be accomplished by implementing and testing these diverse algorithms.This paper is discussing the major description of sentiment analysis, data collection methods, data pre-processing, feature extraction, and sentiment analysis methods related to Twitter data. Real-time data analysis arises as a major method of analyzing the data available online and the real-time Twitter data analysis process is described throughout this paper. Several methods of classifying the polarized Twitter data are discussed within the paper while depicting a proposed method of Twitter data analyzing algorithm. Location-based Twitter data analysis is another crucial aspect of sentiment analyses, that enables data sorting according to geo-location, and this paper describes the way of analyzing Twitter data based on geo-location. Further, a comparison about several sentiment analysis algorithms used by previous researchers has been reported and finally, a conclusion has been provided.

Expressivity of Tweets on Social Issues Using Aspect Based Text Classification

Social discussions about crime on Twitter and open forums aims to understand the barriers that hinder people from expressing their concerns or aligning with popular opinions. A curated dataset spanning three months in 2023 was collected, covering categories like crimes and Gender Equality and Violence Against Women. The study employs aspect-based sentiment analysis to classify sentiment polarity in tweets, utilising a comprehensive framework involving three text feature classification stages. The initial stage analyses individual words, phrases, and tweet patterns to classify text features based on specific linguistic elements. In the subsequent step, semantic relations explore a better understanding of the core sentiment and infer relationships between different text keywords. This stage enhances the analysis by considering the meaning and contextual nuances of the language used in the tweets. The final stage incorporates transformer-based models for effective multilabel classification to view the diversity present in the dataset. The study's quantitative analysis reveals that the Ensemble learning approach demonstrates an impressive precision measure of 93%. By integrating the three stages of text feature classification, the study enhances the accuracy and comprehensiveness of sentiment analysis in social discussions about crime on Twitter

Aspect-based Sentiment Analysis for German: Analyzing Talk of Literature" Surrounding Literary Prizes on Social Media

Since the rise of social media, the authority of traditional professional literary critics has beensupplemented – or undermined, depending on the point of view – by technological developmentsand the emergence of community-driven online layperson literary criticism. So far, relatively littleresearch (Allington 2016, Kellermann et al. 2016, Kellermann and Mehling 2017, Bogaert 2017, Pi-anzola et al. 2020) has examined this layperson user-generated evaluative “talk of literature”instead of addressing traditional forms of consecration. In this paper, we examine the layper-son literary criticism pertaining to a prominent German-language literary award: the Ingeborg-Bachmann-Preis, awarded during the Tage der deutschsprachigen Literatur (TDDL).We propose an aspect-based sentiment analysis (ABSA) approach to discern the evaluativecriteria used to differentiate between ‘good’ and ‘bad’ literature. To this end, we collected a cor-pus of German social media reviews, retrieved from Twitter, and enriched it with manual ABSAannotations:aspectsand aspect categories (e.g. the motifs or themes in a text, the jury discus-sions and evaluations, ...),sentiment expressionsandnamed entities. In a next step, the manualannotations are used as training data for our ABSA pipeline including 1) aspect term categoryprediction and 2) aspect term polarity classification. Each pipeline component is developed usingstate-of-the-art pre-trained BERT models.Two sets of experiments were conducted for the aspect polarity detection: one where only theaspect embeddings were used and another where an additional context window of five adjoiningwords in either direction of the aspect was considered. We present the classification results forthe aspect category and aspect sentiment prediction subtasks for the Twitter corpus. Thesepreliminary experimental results show a good performance for the aspect category classification,with a macro and a weighted F1-score of 69% and 83% for the coarse-grained and 54% and 73% forthe fine-grained task, as well as for the aspect sentiment classification subtask, using an additionalcontext window, with a macro and a weighted F1-score of 70% and 71%, respectivel

Joint Learning of Local and Global Features for Aspect-based Sentiment Classification

Aspect-based sentiment classification (ASC) aims to judge the sentiment polarity conveyed by the given aspect term in a sentence. The sentiment polarity is not only determined by the local context but also related to the words far away from the given aspect term. Most recent efforts related to the attention-based models can not sufficiently distinguish which words they should pay more attention to in some cases. Meanwhile, graph-based models are coming into ASC to encode syntactic dependency tree information. But these models do not fully leverage syntactic dependency trees as they neglect to incorporate dependency relation tag information into representation learning effectively. In this paper, we address these problems by effectively modeling the local and global features. Firstly, we design a local encoder containing: a Gaussian mask layer and a covariance self-attention layer. The Gaussian mask layer tends to adjust the receptive field around aspect terms adaptively to deemphasize the effects of unrelated words and pay more attention to local information. The covariance self-attention layer can distinguish the attention weights of different words more obviously. Furthermore, we propose a dual-level graph attention network as a global encoder by fully employing dependency tag information to capture long-distance information effectively. Our model achieves state-of-the-art performance on both SemEval 2014 and Twitter datasets.Comment: under revie

Semantic Sentiment Analysis of Twitter Data

Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world. This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that was never possible before. Naturally, this abundance of data has quickly attracted business and research interest from various fields including marketing, political science, and social studies, among many others, which are interested in questions like these: Do people like the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about the Brexit? Answering these questions requires studying the sentiment of opinions people express in social media, which has given rise to the fast growth of the field of sentiment analysis in social media, with Twitter being especially popular for research due to its scale, representativeness, variety of topics discussed, as well as ease of public access to its messages. Here we present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition. 201