A Comprehensive Guideline for Bengali Sentiment Annotation

Sentiment Analysis (SA) is a Natural Language Processing (NLP) and an Information Extraction (IE) task that primarily aims to obtain the writer’s feelings expressed in positive or negative by analyzing a large number of documents. SA is also widely studied in the fields of data mining, web mining, text mining, and information retrieval. The fundamental task in sentiment analysis is to classify the polarity of a given content as Positive, Negative, or Neutral . Although extensive research has been conducted in this area of computational linguistics, most of the research work has been carried out in the context of English language. However, Bengali sentiment expression has varying degree of sentiment labels, which can be plausibly distinct from English language. Therefore, sentiment assessment of Bengali language is undeniably important to be developed and executed properly. In sentiment analysis, the prediction potential of an automatic modeling is completely dependent on the quality of dataset annotation. Bengali sentiment annotation is a challenging task due to diversified structures (syntax) of the language and its different degrees of innate sentiments (i.e., weakly and strongly positive/negative sentiments). Thus, in this article, we propose a novel and precise guideline for the researchers, linguistic experts, and referees to annotate Bengali sentences immaculately with a view to building effective datasets for automatic sentiment prediction efficiently.

Heroes and villains: multimodal identity construction in children’s wartime visual narratives

The article investigates the Self and Other binaries in wartime visual literature published in Bengali-language children’s periodicals in West Bengal, India during the Bangladesh Liberation Struggle 1971. The study applies a critical multimodal framework using the Social Actors Approach and Social Semiotics within the Discourse-Historical Approach. The binaries are defined by the representation and subsequent differentiation of physical, linguistic, and cultural features of the Bengali and non-Bengali social actors and through their actions in the plots. The representation of social actors in the texts conforms to as well as deviates from typical wartime propaganda.

Decoding the Impact of the Srirama Panchali on Baranagar Temples Facades: The Driving Force behind Terracotta Artisans’ Narrativization of Ramayana Events

Valmiki Ramayana is one of the most popular, universally read, and widely circulated literary works. The poets of different languages in India ornamented Valmiki’s Sanskrit Ramayana with the vibrancy of their own indigenous languages and cultures. A significant number of such versions trace their roots to Bengal. The epic was first translated into the Bengali language by the great poet Krittibas Ojha. Its influences and popularity have been such as to justify it being called the Bible of the people of Bengal. Its intense undiminished popularity among the populace has also left an indelible impression on the artisans of Bengal and their creations in different eras. The study primarily aims to investigate the Ramayana narratives that have been found on the facades of the temples of Baranagar in Murshidabad, West Bengal, India. The intention is to trace the impact of Krittibas’s Srirama Panchali on the portrayals of the Ramayana episodes. The formal method of Art History has been employed to provide an in-depth description of the formal elements that have been incorporated by the artisans. Besides, a detailed critical inspection of the concerned portrayals has been complemented with literary references to get a lucid understanding of the intended issues.

BengSentiLex and BengSwearLex: creating lexicons for sentiment analysis and profanity detection in low-resource Bengali language

Bengali is a low-resource language that lacks tools and resources for various natural language processing (NLP) tasks, such as sentiment analysis or profanity identification. In Bengali, only the translated versions of English sentiment lexicons are available. Moreover, no dictionary exists for detecting profanity in Bengali social media text. This study introduces a Bengali sentiment lexicon, BengSentiLex, and a Bengali swear lexicon, BengSwearLex. For creating BengSentiLex, a cross-lingual methodology is proposed that utilizes a machine translation system, a review corpus, two English sentiment lexicons, pointwise mutual information (PMI), and supervised machine learning (ML) classifiers in various stages. A semi-automatic methodology is presented to develop BengSwearLex that leverages an obscene corpus, word embedding, and part-of-speech (POS) taggers. The performance of BengSentiLex compared with the translated English lexicons in three evaluation datasets. BengSentiLex achieves 5%–50% improvement over the translated lexicons. For identifying profanity, BengSwearLex achieves documentlevel coverage of around 85% in an document-level in the evaluation dataset. The experimental results imply that BengSentiLex and BengSwearLex are effective resources for classifying sentiment and identifying profanity in Bengali social media content, respectively.

An Open Source Tesseract Based Optical Character Recognizer for Bengali Language

Optical Character Recognition (OCR) is the process of extracting text from an image. The main purpose of an OCR is to make editable documents from existing paper documents or image files. OCR primarily works in two phases; they are character and word detection. In case of more sophisticated approach, an OCR also works on sentence detection to preserve documents’ structures. In this paper, we would discuss the process of developing an OCR for Bengali language. Lots of efforts have been put on developing an OCR for Bengali. Though some OCRs have been developed, none of them is completely error free. For our thesis, we trained Tesseract OCR Engine to develop an OCR for Bengali language. Tesseract is currently the most accurate OCR engine. This engine was developed at HP labs and currently sponsored by Google. In Tesseract there are two option to training first one is Legacy Training and second is LSTM Training. We do both of them.

Using Machine Learning to Detect Events on the Basis of Bengali and Banglish Facebook Posts

In modern times, ensuring social security has become the prime concern for security administrators. The widespread and recurrent use of social media sites is creating a huge risk for the lives of the general people, as these sites are frequently becoming potential sources of the organization of various types of immoral events. For protecting society from these dangers, a prior detection system which can effectively detect events by analyzing these social media data is essential. However, automating the process of event detection has been difficult, as existing processes must account for diverse writing styles, languages, dialects, post lengths, and et cetera. To overcome these difficulties, we developed an effective model for detecting events, which, for our purposes, were classified as either protesting, celebrating, religious, or neutral, using Bengali and Banglish Facebook posts. At first, the collected posts’ text were processed for language detection, and then, detected posts were pre-processed using stopwords removal and tokenization. Features were then extracted from these pre-processed texts using three sub-processes: filtering, phrase matching of specific events, and sentiment analysis. The collected features were ultimately used to train our Bernoulli Naive Bayes classification model, which was capable of detecting events with 90.41% accuracy (for Bengali-language posts) and 70% (for the Banglish-form posts). For evaluating the effectiveness of our proposed model more precisely, we compared it with two other classifiers: Support Vector Machine and Decision Tree.

The Contribution of Women in the Bengali Language Movement (1952): A Historical Analysis

During the period 1947-48, student movements started in various areas of Bangladesh demanding to make Bengali one of the state languages. Through participation in these movements, political awareness among the girls of Bengal increased. So in the final stages of the 1952 language movement, the massive participation of girls can be noticed. The girls of Dhaka and the girls of different districts and sub-divisional cities of Bangladesh took an active part in the 1952 language movement. In addition to school-college girls, various members of various women's organizations such as Shishuraksha Samiti, Wari Mahila Samiti, and others actively take part in the 1952 language movement. Therefore, the role of Bengali women in the Bengali language movement was unforgettable. Apart from men, women also acted as supporting forces of the language movement in various ways from their position. Therefore, the idea which Bengali women are just helpless, helpless is not correct. In this article, we have analyzed the role of women in the Bengali language movement.