Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text

Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark

DravidianCodeMix: Sentiment Analysis and Offensive Language Identification Dataset for Dravidian Languages in Code-Mixed Text

This paper describes the development of a multilingual, manually annotated dataset for three under-resourced Dravidian languages generated from social media comments. The dataset was annotated for sentiment analysis and offensive language identification for a total of more than 60,000 YouTube comments. The dataset consists of around 44,000 comments in Tamil-English, around 7,000 comments in Kannada-English, and around 20,000 comments in Malayalam-English. The data was manually annotated by volunteer annotators and has a high inter-annotator agreement in Krippendorff's alpha. The dataset contains all types of code-mixing phenomena since it comprises user-generated content from a multilingual country. We also present baseline experiments to establish benchmarks on the dataset using machine learning methods. The dataset is available on Github (https://github.com/bharathichezhiyan/DravidianCodeMix-Dataset) and Zenodo (https://zenodo.org/record/4750858\#.YJtw0SYo\_0M).Comment: 36 page

A Survey of Cross-Lingual Sentiment Analysis Based on Pre-Trained Models

With the technology development of natural language processing, many researchers have studied Machine Learning (ML), Deep Learning (DL), monolingual Sentiment Analysis (SA) widely. However, there is not much work on Cross-Lingual SA (CLSA), although it is beneficial when dealing with low resource languages (e.g., Tamil, Malayalam, Hindi, and Arabic). This paper surveys the main challenges and issues of CLSA based on some pre-trained language models and mentions the leading methods to cope with CLSA. In particular, we compare and analyze their pros and cons. Moreover, we summarize the valuable cross-lingual resources and point out the main problems researchers need to solve in the future

Automatic processing of code-mixed social media content

Code-mixing or language-mixing is a linguistic phenomenon where multiple language mix together during conversation. Standard natural language processing (NLP) tools such as part-of-speech (POS) tagger and parsers perform poorly because such tools are generally trained with monolingual content. Thus there is a need for code-mixed NLP. This research focuses on creating a code-mixed corpus in English-Hindi-Bengali and using it to develop a world-level language identifier and a POS tagger for such code-mixed content. The first target of this research is word-level language identification. A data set of romanised and code-mixed content written in English, Hindi and Bengali was created and annotated. Word-level language identification (LID) was performed on this data using dictionaries and machine learn- ing techniques. We find that among a dictionary-based system, a character-n-gram based linear model, a character-n-gram based first order Conditional Random Fields (CRF) and a recurrent neural network in the form of a Long Short Term Memory (LSTM) that consider words as well as characters, LSTM outperformed the other methods. We also took part in the First Workshop of Computational Approaches to Code-Switching, EMNLP, 2014 where we achieved the highest token-level accuracy in the word-level language identification task of Nepali-English. The second target of this research is part-of-speech (POS) tagging. POS tagging methods for code- mixed data (e.g. pipeline and stacked systems and LSTM-based neural models) have been implemented, among them, neural approach outperformed the other approach. Further, we investigate building a joint model to perform language identification and POS tagging jointly. We compare between a factorial CRF (FCRF) based joint model and three LSTM-based multi-task models for word-level language identification and POS tagging. The neural models achieve good accuracy in language identification and POS tagging by outperforming the FCRF approach. Further- more, we found that it is better to go for a multi-task learning approach than to perform individual task (e.g. language identification and POS tagging) using neural approach. Comparison between the three neural approaches revealed that without using task-specific recurrent layers, it is possible to achieve good accuracy by careful handling of output layers for these two tasks e.g. LID and POS tagging

Improving Search via Named Entity Recognition in Morphologically Rich Languages – A Case Study in Urdu

University of Minnesota Ph.D. dissertation. February 2018. Major: Computer Science. Advisors: Vipin Kumar, Blake Howald. 1 computer file (PDF); xi, 236 pages.Search is not a solved problem even in the world of Google and Bing's state of the art engines. Google and similar search engines are keyword based. Keyword-based searching suffers from the vocabulary mismatch problem -- the terms in document and user's information request don't overlap. For example, cars and automobiles. This phenomenon is called synonymy. Similarly, the user's term may be polysemous -- a user is inquiring about a river's bank, but documents about financial institutions are matched. Vocabulary mismatch exacerbated when the search occurs in Morphological Rich Language (MRL). Concept search techniques like dimensionality reduction do not improve search in Morphological Rich Languages. Names frequently occur news text and determine the "what," "where," "when," and "who" in the news text. Named Entity Recognition attempts to recognize names automatically in text, but these techniques are far from mature in MRL, especially in Arabic Script languages. Urdu is one the focus MRL of this dissertation among Arabic, Farsi, Hindi, and Russian, but it does not have the enabling technologies for NER and search. A corpus, stop word generation algorithm, a light stemmer, a baseline, and NER algorithm is created so the NER-aware search can be accomplished for Urdu. This dissertation demonstrates that NER-aware search on Arabic, Russian, Urdu, and English shows significant improvement over baseline. Furthermore, this dissertation highlights the challenges for researching in low-resource MRL languages

Tune your brown clustering, please

Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal

Quantifying the impact of Twitter activity in political battlegrounds

It may be challenging to determine the reach of the information, how well it corresponds with the domain design, and how to utilize it as a communication medium when utilizing social media platforms, notably Twitter, to engage the public in advocating a parliament act, or during a global health emergency. Chapter 3 offers a broad overview of how candidates running in the 2020 US Elections used Twitter as a communication tool to interact with voters. More precisely, it seeks to identify components related to internal collaboration and public participation (in terms of content and stance similarity among the candidates from the same political front and to the official Twitter accounts of their political parties). The 2020 US Presidential and Vice Presidential candidates from the two main political parties, the Republicans and Democrats, are our main subjects. Along with the content similarity, their tweets were assessed for social reach and stance similarity on 22 topics. This study complements previous research on efficiently using social media platforms for election campaigns. Chapter 4 empirically examines the online social associations of the top-10 COVID-19 resilient nations’ leaders and healthcare institutions based on the Bloomberg COVID-19 Resilience Ranking. In order to measure the strength of the online social association in terms of public engagement, sentiment strength, inclusivity and diversity, we used the attributes provided by Twitter Academic Research API, coupled with the tweets of leaders and healthcare organizations from these nations. Understanding how leaders and healthcare organizations may utilize Twitter to establish digital connections with the public during health emergencies is made more accessible by this study. The thesis has proposed methods for efficiently using Twitter in various domains, utilizing the implementations of various Language Models and several data mining and analytics techniques