Multilingual Sentiment Analysis Research Articles

Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape

Sentiment analysis automatically evaluates people’s opinions of products or services. It is an emerging research area with promising advancements in high-resource languages such as Indo-European languages (e.g. English). However, the same cannot be said for languages with limited resources. In this study, we evaluate multilingual sentiment analysis techniques for under-resourced languages and the use of high-resourced languages to develop resources for low-resource languages. The ultimate goal is to identify appropriate strategies for future investigations. We report over 35 studies with different languages demonstrating an interest in developing models for under-resourced languages in a multilingual context. Furthermore, we illustrate the drawbacks of each strategy used for sentiment analysis. Our focus is to critically compare methods, employed datasets and identify research gaps. This study contributes to theoretical literature reviews with complete coverage of multilingual sentiment analysis studies from 2008 to date. Furthermore, we demonstrate how sentiment analysis studies have grown tremendously. Finally, because most studies propose methods based on deep learning approaches, we offer a deep learning framework for multilingual sentiment analysis that does not rely on the machine translation system. According to the meta-analysis protocol of this literature review, we found that, in general, just over 60% of the studies have used deep learning frameworks, which significantly improved the sentiment analysis performance. Therefore, deep learning methods are recommended for the development of multilingual sentiment analysis for under-resourced languages.

Listen to the noise – Demonstrating an end to end multi-platform and multilingual sentiment analysis approach

This paper demonstrates a holistic approach for conducting multi-platform and multilingual sentiment analysis of a main stream football club in the United Arab Emirates. The paper reports first iterations of the research project which anticipates to build an automated sentiment analysis system for football clubs to detect and prevent a potential social media crisis at an early stage. The article illustrates a schematic of how state of the art machine learning algorithms, methods and techniques can be put together to provide a basis for automated data collection, classification, sentiment analysis and visual reporting. The article also presents that the time-consuming and costly process of human text labelling can be effectively replaced by lexicon-based sentiment analysis. This paper reports the complete process from data collection to visual reports to discuss challenges and opportunities associated with design and development of such artefacts.

Using Deep Learning Techniques in Forecasting Stock Markets by Hybrid Data with Multilingual Sentiment Analysis

Electronic word-of-mouth data on social media influences stock trading and the confidence of stock markets. Thus, sentiment analysis of comments related to stock markets becomes crucial in forecasting stock markets. However, current sentiment analysis is mainly in English. Therefore, this study performs multilingual sentiment analysis by translating non-native English-speaking countries’ texts into English. This study used unstructured data from social media and structured data, including trading data and technical indicators, to forecast stock markets. Deep learning techniques and machine learning models have emerged as powerful ways of coping with forecasting problems, and parameter determination greatly influences forecasting models’ performance. This study used Long Short-Term Memory (LSTM) models employing the genetic algorithm (GA) to select parameters for predicting stock market indices and prices of company stocks by hybrid data in non-native English-speaking regions. Numerical results revealed that the developed LSTMGA model with hybrid multilingual sentiment data generates more accurate forecasting than the other machine learning models with various data types. Thus, the proposed LSTMGA model with hybrid multilingual sentiment analysis is a feasible and promising way of forecasting the stock market.

Quantitative questions on big data in translation studies

As corpus-based translation studies continues to expand, researchers have employed data analytic techniques from neighbouring disciplines, such as corpus linguistics, to explore a wider variety of research questions. The field has evolved from early frequency-based approaches to corpus-based translation studies to now include more advanced statistical analyses to understand the complex web of variables encapsulated by the translation process. Big data analytic techniques that originated in data analytics and related quantitative fields could be usefully applied to research questions in translation and interpreting studies. To assess their applicability, this article first outlines what distinguishes big data from general corpora in translation and interpreting studies, identifying how data volume, variety, and velocity are applicable properties to be considered in corpus-based translation and interpreting studies research. Then, the article presents three types of big data analysis techniques, namely crosslingual and multilingual data analysis, sentiment analysis, and visual analysis. These analyses are presented in conjunction with potential research areas that would benefit from these complementary analytical approaches. The article concludes with a discussion of the implications of big data analytics in corpus translation studies, while charting the trajectory of a more quantitative, corpus-based approach to translation studies.

SentiCode: A new paradigm for one-time training and global prediction in multilingual sentiment analysis.

The main objective of multilingual sentiment analysis is to analyze reviews regardless of the original language in which they are written. Switching from one language to another is very common on social media platforms. Analyzing these multilingual reviews is a challenge since each language is different in terms of syntax, grammar, etc. This paper presents a new language-independent representation approach for sentiment analysis, SentiCode. Unlike previous work in multilingual sentiment analysis, the proposed approach does not rely on machine translation to bridge the gap between different languages. Instead, it exploits common features of languages, such as part-of-speech tags used in Universal Dependencies. Equally important, SentiCode enables sentiment analysis in multi-language and multi-domain environments simultaneously. Several experiments were conducted using machine/deep learning techniques to evaluate the performance of SentiCode in multilingual (English, French, German, Arabic, and Russian) and multi-domain environments. In addition, the vocabulary proposed by SentiCode and the effect of each token were evaluated by the ablation method. The results highlight the 70% accuracy of SentiCode, with the best trade-off between efficiency and computing time (training and testing) in a total of about 0.67 seconds, which is very convenient for real-time applications.

Employing machine learning for capturing COVID-19 consumer sentiments from six countries: a methodological illustration

PurposeThis paper aims to illustrate the scope and challenges of using computer-aided content analysis in international marketing with the aim to capture consumer sentiments about COVID-19 from multi-lingual tweets.Design/methodology/approachThe study is based on some 35 million original COVID-19-related tweets. The study methodology illustrates the use of supervised machine learning and artificial neural network techniques to conduct extensive information extraction.FindingsThe authors identified more than two million tweets from six countries and categorized them into PESTEL (i.e. Political, Economic, Social, Technological, Environmental and Legal) dimensions. The extracted consumer sentiments and associated emotions show substantial differences across countries. Our analyses highlight opportunities and challenges inherent in using multi-lingual online sentiment analysis in international marketing. Based on these insights, several future research directions are proposed.Originality/valueFirst, the authors contribute to methodology development in international marketing by providing a “use-case” for computer-aided text mining in a multi-lingual context. Second, the authors add to the knowledge on differences in COVID-19-related consumer sentiments in different countries. Third, the authors provide avenues for future research on the analysis of unstructured multi-media posts.

Stability of a Multilingual Sentiment Analysis based on Word-to-Word Translations

Stability of a Multilingual Sentiment Analysis based on Word-to-Word Translations

MULTILINGUAL SENTIMENT NORMALIZATION FOR SCANDINAVIAN LANGUAGES

In this paper, we address the challenge of multilingual sentiment analysis using a traditional lexicon and rule-based sentiment instrument that is tailored to capture sentiment patterns in a particular language. Focusing on a case study of three closely related Scandinavian languages (Danish, Norwegian, and Swedish) and using three tailored versions of VADER, we measure the relative degree of variation in valence using the OPUS corpus. We found that scores for Swedish are systematically skewed lower than Danish for translational pairs, and that scores for Norwegian are skewed higher for both other languages. We use a neural network to optimize the fit between Norwegian and Swedish respectively and Danish as the reference (target) language.

Sentiment Analysis of Multilingual Tweets Based on Natural Language Processing (NLP)

Multilingual Sentiment analysis plays an important role in a country like India with many languages as the style of expression varies in different languages. The Indian people speak in total 22 different languages and with the help of Google Indic keyboard people can express their sentiments i.e reviews about anything in the social media in their native language from individual smart phones. It has been found that machine learning approach has overcome the limitations of other approaches. In this paper, a detailed study has been carried out based on Natural Language Processing (NLP) using Simple Neural Network (SNN) ,Convolutional Neural Network(CNN), and Long Short Term Memory (LSTM)Neural Network followed by another amalgamated model adding a CNN layer on top of the LSTM without worrying about versatility of multilingualism. Around 4000 samples of reviews in English, Hindi and in Bengali languages are considered to generate outputs for the above models and analyzed. The experimental results on these realistic reviews are found to be effective for further research work.

Sentiment Analysis of Multilingual Tweets based on Natural Language Processing (NLP)

Multilingual Sentiment analysis plays an important role in a country like India with many languages as the style of expression varies in different languages. The Indian people speak in total 22 different languages and with the help of Google Indic keyboard people can express their sentiments i.e reviews about anything in the social media in their native language from individual smart phones. It has been found that machine learning approach has overcome the limitations of other approaches. In this paper, a detailed study has been carried out based on Natural Language Processing (NLP) using Simple Neural Network (SNN) ,Convolutional Neural Network(CNN), and Long Short Term Memory (LSTM)Neural Network followed by another amalgamated model adding a CNN layer on top of the LSTM without worrying about versatility of multilingualism. Around 4000 samples of reviews in English, Hindi and in Bengali languages are considered to generate outputs for the above models and analyzed. The experimental results on these realistic reviews are found to be effective for further research work.

Deep learning and multilingual sentiment analysis on social media data: An overview

Twenty-four studies on twenty-three distinct languages and eleven social media illustrate the steady interest in deep learning approaches for multilingual sentiment analysis of social media. We improve over previous reviews with wider coverage from 2017 to 2020 as well as a study focused on the underlying ideas and commonalities behind the different solutions to achieve multilingual sentiment analysis. Interesting findings of our research are (i) the shift of research interest to cross-lingual and code-switching approaches, (ii) the apparent stagnation of the less complex architectures derived from a backbone featuring an embedding layer, a feature extractor based on a single CNN or LSTM and a classifier, (iii) the lack of approaches tackling multilingual aspect-based sentiment analysis through deep learning, and, surprisingly, (iv) the lack of more complex architectures such as the transformers-based, despite results suggest the more difficult tasks requires more elaborated architectures.

Multilingual Review-aware Deep Recommender System via Aspect-based Sentiment Analysis

With the dramatic expansion of international markets, consumers write reviews in different languages, which poses a new challenge for Recommender Systems (RSs) dealing with this increasing amount of multilingual information. Recent studies that leverage deep-learning techniques for review-aware RSs have demonstrated their effectiveness in modelling fine-grained user-item interactions through the aspects of reviews. However, most of these models can neither take full advantage of the contextual information from multilingual reviews nor discriminate the inherent ambiguity of words originated from the user’s different tendency in writing. To this end, we propose a novel Multilingual Review-aware Deep Recommendation Model (MrRec) for rating prediction tasks. MrRec mainly consists of two parts: (1) Multilingual aspect-based sentiment analysis module (MABSA), which aims to jointly extract aligned aspects and their associated sentiments in different languages simultaneously with only requiring overall review ratings. (2) Multilingual recommendation module that learns aspect importances of both the user and item with considering different contributions of multiple languages and estimates aspect utility via a dual interactive attention mechanism integrated with aspect-specific sentiments from MABSA. Finally, overall ratings can be inferred by a prediction layer adopting the aspect utility value and aspect importance as inputs. Extensive experimental results on nine real-world datasets demonstrate the superior performance and interpretability of our model.

Multilingual Sentiment Analysis: A Systematic Literature Review

With the explosive growth of social media, the online community can freely express their opinions without disclosing their identities. People with hidden agendas can easily post fake opinions to discredit target products, services, politicians, or organizations. With these big data, monitoring opinions and distilling their sentiments remain a formidable task because of the proliferation of diverse sites with a large volume of opinions that are portrayed in multilingual. Therefore, this paper aims to provide a systematic literature review on multilingual sentiment analysis, which summarises the common languages supported in multilingual sentiment analysis, pre-processing techniques, existing sentiment analysis approaches, and evaluation models that have been used for multilingual sentiment analysis. By following the systematic literature review, the findings revealed, most of the models supported two languages, and English is seen as the most used language in sentiment analysis studies. None of the reviewed literature has catered the combination of languages for English, Chinese, Malay, and Hindi language on multilingual sentiment analysis. The common pre-processing techniques for the multilingual domain are tokenization, normalization, capitalization, N-gram, and machine translation. Meanwhile, the sentiment analysis classification techniques for multilingual sentiment are hybrid sentiment analysis, which includes localized language analysis, unsupervised topic clustering, and then followed by multilingual sentiment analysis. In terms of evaluation, most of the studies used precision, recall, and accuracy as the benchmark for the results.

An Enhanced Feature Acquisition for Sentiment Analysis of English and Hausa Tweets

Due to the continuous and rapid growth of social media, opinionated contents are actively created by users in different languages about various products, services, events, and political parties. The automated classification of these contents prompted the need for multilingual sentiment analysis researches. However, the majority of research efforts are devoted to English and Arabic, English and German, English and French languages, while a great share of information is available in other languages such as Hausa. This paper proposes multilingual sentiment analysis of English and Hausa tweets using an Enhanced Feature Acquisition Method (EFAM). The method uses machine learning approach to integrate two newly defined Hausa features (Hausa Lexical Feature and Hausa Sentiment Intensifiers) and English feature to measure classification performance and to synthesize a more accurate sentiment classification procedure. The approach has been evaluated using several experiments with different classifiers in both monolingual and multilingual datasets. The experimental results reveal the effectiveness of the approach in enhancing feature integration for multilingual sentiment analysis. Similarly, by using features drawn from multiple languages, we can construct machine learning classifiers with an average precision of over 65%.

Multilingual twitter sentiment analysis using machine learning

Twitter sentiment analysis is one of the leading research fields. Most of the researchers were contributed to twitter sentiment analysis in English tweets, but few researchers focus on the multilingual twitter sentiment analysis. Some challenges are hoping for the research solutions in multilingual twitter sentiment analysis. This study presents the implementation of sentiment analysis in multilingual twitter data and improves the data classification up to the adequate level of accuracy. Twitter is the sixth leading social networking site in the world. Active users for twitter in a month are 330 million. People can tweet or re-tweet in their languages and allow users to use emoji’s, abbreviations, contraction words, miss spellings, and shortcut words. The best platform for sentiment analysis is twitter. Multilingual tweets and data sparsity are the two main challenges. In this paper, the MLTSA algorithm gives the solution for these two challenges. MLTSA algorithm divides into two parts. One is detecting and translating non-English tweets into English using natural language processing (NLP). And the second one is an appropriate pre-processing method with NLP support can reduce the data sparsity. The result of the MLTSA with SVM achieves good accuracy by up to 95%.

A Review on Multi-Lingual Sentiment Analysis by Machine Learning Methods

A Review on Multi-Lingual Sentiment Analysis by Machine Learning Methods

Language Identification for Multilingual Sentiment Examination

Social media is most popular platform on which users can share their views, reviews and knowledge about various topics, news, products etc. Identifying sentiments or opinions of users is valuable for many e-commerce companies, Hotels, e-learning etc. This opinion analysis is useful for companies to improve their service and products. Due to increase in web users across globe, users happen to post their views freely over the internet. Many different languages are spoken across globe, supporting multilingual nature of social media makes analysis of such text difficult. Sentiment analysis can be conducted using videos, image, text, where text sentiment analysis is most popular form because of freely available contents in the form of blogs, reviews, comments etc. Because of development of social media platform, people can post comment in any language, creates the need for Multilingual sentiment analysis. Sentiment analysis task needs phases such as data collection, pre-processing, sentiment classification and polarity identification. The Multilingual nature needs Script Identification on the input text by labelling the different words used in text along with scripts used to denote them. Various languages used in the text are identified and the Hindi language text written in Romanized script is transliterated to Devanagari script. Text is then completely translated into English language and POS(Parts of Speech) tagging is performed on the obtained text. The aim and purpose of this study is to survey different techniques of multilingual sentiment analysis, and language identification of source text, where n-grams model outperforms all.

A comparative study of machine translation for multilingual sentence-level sentiment analysis

Sentiment analysis has become a key tool for several social media applications, including, analysis of user’s opinions about products and services, support for politics during campaigns and even identification of market trending. Multiple existing sentiment analysis methods explore different techniques, usually relying on lexical resources or learning approaches. Despite the significant interest in this theme and amount of research efforts in the field, almost all existing methods are designed to work with only English content. Most current strategies in other languages consist of adapting existing lexical resources, without presenting proper validations and basic baseline comparisons. In this work, we take a different step into this field. We focus on evaluating existing efforts proposed to do language specific sentiment analysis with a simple yet effective baseline approach. To do it, we evaluated sixteen methods for sentence-level sentiment analysis proposed for English, and compared them with three language-specific methods. Based on fourteen human labeled language-specific datasets, we provide an extensive quantitative analysis of existing multilingual approaches. Our results suggest that simply translating the input text in a specific language to English and then using one of the existing best methods developed for English can be better than the existing language-specific approach evaluated. We also rank methods according to their prediction performance and identify those that acquired the best results using machine translation across different languages. As a final contribution to the research community, we release our codes, datasets, and the iFeel 3.0 system, a Web framework and tool for multilingual sentence-level sentiment analysis11iFeel resources: https://sites.google.com/view/ifeel-resources/home.. We hope our system sets up a new baseline for future sentence-level methods developed in a wide set of languages.

Multilingual Sentiment Analysis: A New Approach to Measuring Conflict in Legislative Speeches

Comparative scholars of legislative politics continue to face the challenge of measuring a key theoretical concept: conflict at the level of legislative bills. We address this challenge with a multilingual sentiment‐based approach and show that such a measure can effectively capture different types of parliamentary conflict. We also demonstrate that an automated translation of the dictionary yields valid results and therefore greatly facilitates comparative work on legislatures. Our applications show that a sentiment approach recovers government‐opposition dynamics in various settings. The use of a simple, translatable sentiment dictionary opens up the possibility of studying legislative conflict in bill debates across languages and countries.

Sentiment Analysis of Code-Mixed Bambara-French Social Media Text Using Deep Learning Techniques

The global growth of the Internet and the rapid expansion of social networks such as Facebook make multilingual sentiment analysis of social media content very necessary. This paper performs the first sentiment analysis on code-mixed Bambara-French Facebook comments. We develop four Long Short-term Memory (LSTM)-based models and two Convolutional Neural Network (CNN)-based models, and use these six models, Naive Bayes, and Support Vector Machines (SVM) to conduct experiments on a constituted dataset. Social media text written in Bambara is scarce. To mitigate this weakness, this paper uses dictionaries of character and word indexes to produce character and word embedding in place of pre-trained word vectors. We investigate the effect of comment length on the models and perform a comparison among them. The best performing model is a one-layer CNN deep learning model with an accuracy of 83.23 %.