Exploring Word Embedding for Bangla Sentiment Analysis

Sumit, Sakhawat Hosain; Hossan, Md. Zakir; Muntasir, Tareq Al; Sourov, Tanvir

doi:10.1109/icbslp.2018.8554443

Cited by 20 publications

(10 citation statements)

References 4 publications

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“…As a result of the research, the authors concluded that the "term presence" is more important than the "term frequency" in SA; adjective, adverb, and verbs can be considered as features and irrelevant words can be removed from the corpus so that vocab size can be reduced (Mejova & Srinivasan, 2011). In addition, the authors indicated that most researchers performed SA using English language; but they found some researchers used non-English languages for solving SA problems providing compatible results as well (Che et al, 2015;Sharma et al, 2015;Sumit et al, 2018, as cited in Sharma et al, 2020.…”

Section: Resultsmentioning

confidence: 99%

Machine learning for sentiment analysis in social networks data: Advances and perspectives

Pérez-Pérez

2023

CIERMMI Women in Science Engineering Sciences and Applications

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Sentiment analysis is a field of study within artificial intelligence aimed at comprehending opinions and emotions expressed in natural language, such as texts published on social networks. Social networks are understood as online technologies, tools, and applications that allow users to generate content, share and exchange information, and create interpersonal and communal relationships through the Internet

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Section: Resultsmentioning

confidence: 99%

Machine learning for sentiment analysis in social networks data: Advances and perspectives

Pérez-Pérez

2023

CIERMMI Women in Science Engineering Sciences and Applications

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“…The Facebook AI Research (FAIR) team developed a library and toolkit for effective text classification and word representation. It was created to handle text data effectively and efficiently, especially in situations where there is a lot of text and there aren't many computational resources available [24]. Word embeddings, which are vector representations of words in a continuous space, can be created using FastText [16].…”

Section: Data Collection and Processingmentioning

confidence: 99%

“…A text classification model that links textual descriptions to backdrop image names is trained using FastText. The resulting model can anticipate background names from fresh descriptions, making it easier for comic strips and graphic storytelling to seamlessly combine text and graphics [24].…”

Section: Model Trainingmentioning

confidence: 99%

Generate Comic Strips Using AI

Pramoda P Gunasekara,

Pawani Muthusala Perera,

Chathum D Adhihetty

et al. 2024

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The art of storytelling through comic strips has long been a favorite, grabbing the interest of readers of all ages and developing into a variety of genres today, from superhero comics to political comics. Comic strip readers are more literate, empathic, and open to social issues, according to studies. Comics have the power to change the way we see the world. This research paper presents a system (ComicGenie) for automatically creating comic strips for Batman based on user-entered text descriptions. The proposed system consists of distinct components such as character detection, environment detection, text bubble generation, and voiceover over the scenario, each specializing in different aspects of comic strip creation. SVM and fastText technologies in NLP were utilized for text classification when developing models. Overall, this research contributes to the field of comic strip creation, offering a comprehensive web application. The ComicGenie holds significant potential to revolutionize the comic industry and inspire new avenues for computer-generated storytelling.

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“…However, the sentimental analysis of the Bengali digital data does not yield a good result due to the unavailability of a good sentimental analyzer. Sumit et al [17] have used the word2vec model for representation vectors from Bengali text where skip-gram has been used to find out the close words to understand the sentiment of a Bengali sentence. The skip-gram and word2vec model provide 83.79% accuracy in the sentimental analysis of the Bengali data [17].…”

Section: Related Workmentioning

confidence: 99%

Medical nearest-word embedding technique implemented using an unsupervised machine learning approach for Bengali language

Mandal,

Mukherjee,

Vishnu

et al. 2024

International Journal on Smart Sensing and Intelligent Systems

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The rapid growth of natural language processing (NLP) applications, such as text summarization, speech recognition, information extraction, and machine translation, has led to the development of structured query language (SQL) for extracting information from structured data. However, due to limited resources, converting Natural Language (NL) queries to SQL in Bengali is challenging. This article proposes an unsupervised machine learning model to find semantically Bengali closed words that can generate SQL from NL queries in Bengali. The main objective of the proposed system is to provide support in the creation of patient-oriented explanations and educational resources by simplifying intricate medical terminology. The major findings of the proposed system are as follows: The use of machine translation in the field of medicine facilitates the dissemination of healthcare information to a diverse international audience and improves the performance of entity recognition tasks, including the identification of medical conditions, drugs, or procedures within clinical notes or electronic health data. This system allows a naive user to extract health-related information from a healthcare-structured database without any knowledge of SQL. The system accepts a query and generates a response according to the query in Bengali language. Query tokenization and stop word removal are carried out in the preprocessing stage, and unsupervised machine learning techniques are implemented to process the input query sentence. Tokenized words are converted into vectors using the skip-gram model, with noise-contrastive estimation (NCE) applied to discriminate between actual and irrelevant words. Stochastic gradient descent (SGD) optimizes the model by randomly choosing a small amount of data from the dataset and using cosine similarity to measure closer words. The semantically closer words are found using an unsupervised learning method to generate the SQL.

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Exploring Word Embedding for Bangla Sentiment Analysis

Cited by 20 publications

References 4 publications

Machine learning for sentiment analysis in social networks data: Advances and perspectives

Machine learning for sentiment analysis in social networks data: Advances and perspectives

Generate Comic Strips Using AI

Medical nearest-word embedding technique implemented using an unsupervised machine learning approach for Bengali language

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