Multilingual Sentiment Research Articles

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%.

Covhindia: Deep Learning Framework for Sentiment Polarity Detection of Covid-19 Tweets in Hindi

On 11th March 2020, the World Health Organization (WHO) declared Corona Virus Disease of 2019 (COVID-19) as a pandemic. Over time, the exponential growth of this disease has highlighted a mixture of sentiments expressed by the general population from various parts of the world speaking varied languages. It is, therefore, essential to analyze the public sentiment during this wave of the pandemic. While much work prevails to determine the sentiment polarity for tweets related to COVID-19, expressed in the English language, we still need to work on public sentiments expressed in languages other than English. This paper proposes a framework, Covhindia, a deep-learning framework that performs sentiment polarity detection of tweets related to COVID-19 posted in the Hindi language on the Twitter platform. The proposed framework leverages machine translation on Hindi tweets and passes the translated data as input to a deep learning model which is trained on an English corpus of COVID-19 tweets posted from India [18]. The paper compares nine deep learning models' performances in classifying the sentiment polarity on an English dataset. Performance comparison of these architectures reveals that the BERT model had the best polarity detection accuracy on the English corpus. As part of testing the Covhindia’s accuracy in performing sentiment classification on Hindi tweets, the paper employs a separate dataset developed using a python library called Tweepy to extract Hindi tweets related to COVID-19. Experimental results reveal that Covhindia achieved state-of-the-art accuracy in classifying COVID-19 tweets posted in the Hindi language. The use of open-source machine translation tools paved the way for leveraging Covhindia for performing multilingual sentiment classification on COVID-19 tweets. For the benefit of the research community, the code and Jupyter Notebooks related to this paper are available on Github

COVHINDIA: DEEP LEARNING FRAMEWORK FOR SENTIMENT POLARITY DETECTION OF COVID-19 TWEETS IN HINDI

On 11th March 2020, the World Health Organization (WHO) declared Corona Virus Disease of 2019 (COVID-19) as a pandemic. Over time, the exponential growth of this disease has highlighted a mixture of sentiments expressed by the general population from various parts of the world speaking varied languages. It is, therefore, essential to analyze the public sentiment during this wave of the pandemic. While much work prevails to determine the sentiment polarity for tweets related to COVID-19, expressed in the English language, we still need to work on public sentiments expressed in languages other than English. This paper proposes a framework, Covhindia, a deep-learning framework that performs sentiment polarity detection of tweets related to COVID-19 posted in the Hindi language on the Twitter platform. The proposed framework leverages machine translation on Hindi tweets and passes the translated data as input to a deep learning model which is trained on an English corpus of COVID-19 tweets posted from India [18]. The paper compares nine deep learning models' performances in classifying the sentiment polarity on an English dataset. Performance comparison of these architectures reveals that the BERT model had the best polarity detection accuracy on the English corpus. As part of testing the Covhindia’s accuracy in performing sentiment classification on Hindi tweets, the paper employs a separate dataset developed using a python library called Tweepy to extract Hindi tweets related to COVID-19. Experimental results reveal that Covhindia achieved state-of-the-art accuracy in classifying COVID-19 tweets posted in the Hindi language. The use of open-source machine translation tools paved the way for leveraging Covhindia for performing multilingual sentiment classification on COVID-19 tweets. For the benefit of the research community, the code and Jupyter Notebooks related to this paper are available on Github1. .

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 %.

#europehappinessmap: A Framework for Multi-Lingual Sentiment Analysis via Social Media Big Data (A Twitter Case Study)

The growth and popularity of social media platforms have generated a new social interaction environment thus a new collaboration and communication network among individuals. These platforms own tremendous amount of data about users’ behaviors and sentiments since people create, share or exchange their information, ideas, pictures or video using them. One of these popular platforms is Twitter, which via its voluntary information sharing structure, provides researchers data potential of benefit for their studies. Based on Twitter data, in this study a multilingual sentiment detection framework is proposed to compute European Gross National Happiness (GNH). This framework consists of a novel data collection, filtering and sampling method, and a newly constructed multilingual sentiment detection algorithm for social media big data, and tested with nine European countries (United Kingdom, Germany, Sweden, Turkey, Portugal, The Netherlands, Italy, France and Spain) and their national languages over a six year period. The reliability of the data is checked with peak/troughs comparison for special days from Wikipedia news lists. The validity is checked with a group of correlation analyses with OECD Life Satisfaction survey reports’, Euro-Dollar and other currency exchanges, and national stock market time series data. After validity and reliability confirmations, the European GNH map is drawn for six years. The main problem addressed is to propose a novel multilingual social media sentiment analysis framework for calculating GNH for countries and change the way of OECD type organizations’ survey and interview methodology. Also, it is believed that this framework can serve more detailed results (e.g., daily or hourly sentiments of society in different languages).

Defining a Gold Standard for a Swedish Sentiment Lexicon

There is an increasing demand for multilingual sentiment analysis, and most work on sentiment lexicons is still carried out based on English lexicons like WordNet. In addition, many of the non-English sentiment lexicons that do exist have been compiled by (machine) translation from English resources, thereby arguably obscuring possible language-specific characteristics of sentiment-loaded vocabulary. In this paper we describe the creation from scratch of a gold standard for the sentiment annotation of Swedish terms as a first step towards the creation of a full-fledged sentiment lexicon for Swedish.

An unsupervised multilingual approach for online social media topic identification

Social media data can be valuable in many ways. However, the vast amount of content shared and the linguistic variants of languages used on social media are making it very challenging for high-value topics to be identified. In this paper, we present an unsupervised multilingual approach for identifying highly relevant terms and topics from the mass of social media data. This approach combines term ranking, localised language analysis, unsupervised topic clustering and multilingual sentiment analysis to extract prominent topics through analysis of Twitter's tweets from a period of time. It is observed that each of the ranking methods tested has their strengths and weaknesses, and that our proposed ‘Joint’ ranking method is able to take advantage of the strengths of the ranking methods. This ‘Joint’ ranking method coupled with an unsupervised topic clustering model is shown to have the potential to discover topics of interest or concern to a local community. Practically, being able to do so may help decision makers to gauge the true opinions or concerns on the ground. Theoretically, the research is significant as it shows how an unsupervised online topic identification approach can be designed without much manual annotation effort, which may have great implications for future development of expert and intelligent systems.

Multilingual visual sentiment concept clustering and analysis

Visual content is a rich medium that can be used to communicate not only facts and events, but also emotions and opinions. In some cases, visual content may carry a universal affective bias (e.g., natural disasters or beautiful scenes). Often however, to achieve a parity in the affections a visual media invokes in its recipient compared to the one an author intended requires a deep understanding and even sharing of cultural backgrounds. In this study, we propose a computational framework for the clustering and analysis of multilingual visual affective concepts used in different languages which enable us to pinpoint alignable differences (via similar concepts) and nonalignable differences (via unique concepts) across cultures. To do so, we crowdsource sentiment labels for the MVSO dataset, which contains 16 K multilingual visual sentiment concepts and 7.3M images tagged with these concepts. We then represent these concepts in a distribution-based word vector space via (1) pivotal translation or (2) cross-lingual semantic alignment. We then evaluate these representations on three tasks: affective concept retrieval, concept clustering, and sentiment prediction—all across languages. The proposed clustering framework enables the analysis of the large multilingual dataset both quantitatively and qualitatively. We also show a novel use case consisting of a facial image data subset and explore cultural insights about visual sentiment concepts in such portrait-focused images.

Multilingual emotion classification using supervised learning: Comparative experiments

The importance of emotion mining is acknowledged in a wide range of new applications, thus broadening the potential market already proven for opinion mining. However, the lack of resources for languages other than English is even more critical for emotion mining. In this article, we investigate whether Multilingual Sentiment Analysis delivers reliable and effective results when applied to emotions. For this purpose, we developed experiments involving machine translations over corpora originally written in two languages. Our experimental framework for emotion classification assesses variations on (i) the language of the original text and its translations; (ii) strategies to combine multiple languages to overcome losses due to translation; (iii) options for data pre-processing (tokenization, feature representation and feature selection); and (iv) classification algorithms, including meta-classifiers. The results show that emotion classification performance improve significantly with the use of texts in multiple languages, particularly by adopting a stacking of weak monolingual classifiers. Our study also sheds light into the impacts of data preparation strategies and their combination with classification algorithms, and compares differences between polarity and emotion classification according to the same experimental settings.

Universal, unsupervised (rule-based), uncovered sentiment analysis

We present a novel unsupervised approach for multilingual sentiment analysis driven by compositional syntax-based rules. On the one hand, we exploit some of the main advantages of unsupervised algorithms: (1) the interpretability of their output, in contrast with most supervised models, which behave as a black box and (2) their robustness across different corpora and domains. On the other hand, by introducing the concept of compositional operations and exploiting syntactic information in the form of universal dependencies, we tackle one of their main drawbacks: their rigidity on data that are structured differently depending on the language concerned. Experiments show an improvement both over existing unsupervised methods, and over state-of-the-art supervised models when evaluating outside their corpus of origin. Experiments also show how the same compositional operations can be shared across languages. The system is available at http://www.grupolys.org/software/UUUSA/.

Multilingual sentiment analysis: from formal to informal and scarce resource languages

The ability to analyse online user-generated content related to sentiments (e.g., thoughts and opinions) on products or policies has become a de-facto skillset for many companies and organisations. Besides the challenge of understanding formal textual content, it is also necessary to take into consideration the informal and mixed linguistic nature of online social media languages, which are often coupled with localised slang as a way to express ‘true’ feelings. Due to the multilingual nature of social media data, analysis based on a single official language may carry the risk of not capturing the overall sentiment of online content. While efforts have been made to understand multilingual sentiment analysis based on a range of informal languages, no significant electronic resource has been built for these localised languages. This paper reviews the various current approaches and tools used for multilingual sentiment analysis, identifies challenges along this line of research, and provides several recommendations including a framework that is particularly applicable for dealing with scarce resource languages.

Erratum to: Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques

Erratum to: Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques

Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques.

With the advent of Internet, people actively express their opinions about products, services, events, political parties, etc., in social media, blogs, and website comments. The amount of research work on sentiment analysis is growing explosively. However, the majority of research efforts are devoted to English-language data, while a great share of information is available in other languages. We present a state-of-the-art review on multilingual sentiment analysis. More importantly, we compare our own implementation of existing approaches on common data. Precision observed in our experiments is typically lower than the one reported by the original authors, which we attribute to the lack of detail in the original presentation of those approaches. Thus, we compare the existing works by what they really offer to the reader, including whether they allow for accurate implementation and for reliable reproduction of the reported results.

Sentiment analysis system adaptation for multilingual processing: The case of tweets

Nowadays opinion mining systems play a strategic role in different areas such as Marketing, Decision Support Systems or Policy Support. Since the arrival of the Web 2.0, more and more textual documents containing information that express opinions or comments in different languages are available. Given the proven importance of such documents, the use of effective multilingual opinion mining systems has become of high importance to different fields. This paper presents the experiments carried out with the objective to develop a multilingual sentiment analysis system. We present initial evaluations of methods and resources performed in two international evaluation campaigns for English and for Spanish. After our participation in both competitions, additional experiments were carried out with the aim of improving the performance of both Spanish and English systems by using multilingual machine-translated data. Based on our evaluations, we show that the use of hybrid features and multilingual, machine-translated data (even from other languages) can help to better distinguish relevant features for sentiment classification and thus increase the precision of sentiment analysis systems.

MuSES: Multilingual Sentiment Elicitation System for Social Media Data

A multilingual sentiment identification system (MuSES) implements three different sentiment identification algorithms. The first algorithm augments previous compositional semantic rules by adding rules specific to social media. The second algorithm defines a scoring function that measures the degree of a sentiment, instead of simply classifying a sentiment into binary polarities. All such scores are calculated based on a large volume of customer reviews. Due to the special characteristics of social media texts, a third algorithm takes emoticons, negation word position, and domain-specific words into account. In addition, a proposed label-free process transfers multilingual sentiment knowledge between different languages. The authors conduct their experiments on user comments from Facebook, tweets from Twitter, and multilingual product reviews from Amazon.