Multi-lingual Opinion Mining on YouTube

In order to successfully apply opinion mining (OM) to the large amounts of user-generated content produced every day, we need robust models that can handle the noisy input well yet can easily be adapted to a new domain or language. We here focus on opinion mining for YouTube by (i) modeling classifiers that predict the type of a comment and its polarity, while distinguishing whether the polarity is directed towards the product or video; (ii) proposing a robust shallow syntactic structure (STRUCT) that adapts well when tested across domains; and (iii) evaluating the effectiveness on the proposed structure on two languages, English and Italian. We rely on tree kernels to automatically extract and learn features with better generalization power than traditionally used bag-of-word models. Our extensive empirical evaluation shows that (i) STRUCT outperforms the bag-of-words model both within the same domain (up to 2.6% and 3% of absolute improvement for Italian and English, respectively); (ii) it is particularly useful when tested across domains (up to more than 4% absolute improvement for both languages), especially when little training data is available (up to 10% absolute improvement) and (iii) the proposed structure is also effective in a lower-resource language scenario, where only less accurate linguistic processing tools are available

A Comprehensive Review of Sentiment Analysis on Indian Regional Languages: Techniques, Challenges, and Trends

Sentiment analysis (SA) is the process of understanding emotion within a text. It helps identify the opinion, attitude, and tone of a text categorizing it into positive, negative, or neutral. SA is frequently used today as more and more people get a chance to put out their thoughts due to the advent of social media. Sentiment analysis benefits industries around the globe, like finance, advertising, marketing, travel, hospitality, etc. Although the majority of work done in this field is on global languages like English, in recent years, the importance of SA in local languages has also been widely recognized. This has led to considerable research in the analysis of Indian regional languages. This paper comprehensively reviews SA in the following major Indian Regional languages: Marathi, Hindi, Tamil, Telugu, Malayalam, Bengali, Gujarati, and Urdu. Furthermore, this paper presents techniques, challenges, findings, recent research trends, and future scope for enhancing results accuracy

Transfer Learning for Low-Resource Sentiment Analysis

Sentiment analysis is the process of identifying and extracting subjective information from text. Despite the advances to employ cross-lingual approaches in an automatic way, the implementation and evaluation of sentiment analysis systems require language-specific data to consider various sociocultural and linguistic peculiarities. In this paper, the collection and annotation of a dataset are described for sentiment analysis of Central Kurdish. We explore a few classical machine learning and neural network-based techniques for this task. Additionally, we employ an approach in transfer learning to leverage pretrained models for data augmentation. We demonstrate that data augmentation achieves a high F$_1$ score and accuracy despite the difficulty of the task.Comment: 14 pages - under review at ACM TALLI

RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using Weighted and Majority Voted Fine-Tuned Transformers

This paper describes our approach to submissions made at Shared Task 2 at BLP Workshop - Sentiment Analysis of Bangla Social Media Posts. Sentiment Analysis is an action research area in the digital age. With the rapid and constant growth of online social media sites and services and the increasing amount of textual data, the application of automatic Sentiment Analysis is on the rise. However, most of the research in this domain is based on the English language. Despite being the world's sixth most widely spoken language, little work has been done in Bangla. This task aims to promote work on Bangla Sentiment Analysis while identifying the polarity of social media content by determining whether the sentiment expressed in the text is Positive, Negative, or Neutral. Our approach consists of experimenting and finetuning various multilingual and pre-trained BERT-based models on our downstream tasks and using a Majority Voting and Weighted ensemble model that outperforms individual baseline model scores. Our system scored 0.711 for the multiclass classification task and scored 10th place among the participants on the leaderboard for the shared task. Our code is available at https://github.com/ptnv-s/RSM-NLP-BLP-Task2 .Comment: Accepted at The 1st Workshop on Bangla Language Processing (BLP 2023), EMNLP 202

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