A Comprehensive Guideline for Bengali Sentiment Annotation

Author(s):  
Md. Saddam Hossain Mukta ◽  
Md. Adnanul Islam ◽  
Faisal Ahamed Khan ◽  
Afjal Hossain ◽  
Shuvanon Razik ◽  
...  

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.

Author(s):  
Subhadip Chandra ◽  
Randrita Sarkar ◽  
Sayon Islam ◽  
Soham Nandi ◽  
Avishto Banerjee ◽  
...  

Sentiment analysis is the methodical recognition, extraction, quantification, and learning of affective states and subjective information using natural language processing, text analysis, computational linguistics, and biometrics. People frequently use Twitter, one of numerous popular social media platforms, to convey their thoughts and opinions about a business, a product, or a service. Analysis of tweet sentiments is particularly useful in detecting if people have a good, negative, or neutral opinion. This study assesses public opinion about an individual, activity, commodity, or organization. The Twitter API is utilised in this article to directly get tweets from Twitter and develop a sentiment categorization for the tweets. This paper has used Twitter data for two separate approaches, viz., Lexicon & Machine Learning. Lexicon based approach further categorized in Corpus-based and Dictionary-based. And various Machine learning-based approaches like Support Vector Machine (SVM), Naïve Bayes, Maximum entropy are used to analyse Twitter data. Neural Network (NN), Decision tree-based sentiment analysis is also covered in this research work, to find out better accuracy of the approaches in the various data range. Graphs and confusion matrices are used to visualise the results of the analysis for positive, negative, and neutral remarks regarding their opinions.


2020 ◽  
Vol 11 (4) ◽  
pp. 31-44
Author(s):  
Amala Jayanthi M. ◽  
Elizabeth Shanthi I.

Educational data mining is a research field that is used to enhance education system. Research studies using educational data mining are in increase because of the knowledge acquired for decision making to enhance the education process by the information retrieved by machine learning processes. Sentiment analysis is one of the most involved research fields of data mining in natural language processing, web mining, and text mining. It plays a vital role in many areas such as management sciences and social sciences, including education. In education, investigating students' opinions, emotions using techniques of sentiment analysis can understand the students' feelings that students experience in academic, personal, and societal environments. This investigation with sentiment analysis helps the academicians and other stakeholders to understand their motive on education is online. This article intends to explore different theories on education, students' learning process, and to study different approaches of sentiment analysis academics.


2020 ◽  
pp. 422-439
Author(s):  
Nilesh M Shelke ◽  
Shrinivas P Deshpande

Sentiment analysis is an extension of data mining which employs natural language processing and information extraction task to recognize people's opinion towards entities such as products, services, issues, organizations, individuals, events, topics, and their attributes. It gives the summarized opinion of a writer or speaker. It has received lot of attention due to increasing number of posts/tweets on social sites. The proposed system is meant to classify a given text of review into positive, negative, or the neutral category. Primary objective of this article is to provide a method of exploiting permutation and combination and chi values for sentiment analysis of product reviews. Publicly available freely dictionary SentiWordNet 3.0 has been used for review classification. The proposed system is domain independent and context aware. Another objective of the proposed system is to identify the feature specific intensity with which reviewer has expressed his opinion. Effectiveness of the proposed system has been verified through performance matrix and compared with other research work.


2018 ◽  
Vol 9 (2) ◽  
pp. 76-93
Author(s):  
Nilesh M Shelke ◽  
Shrinivas P Deshpande

Sentiment analysis is an extension of data mining which employs natural language processing and information extraction task to recognize people's opinion towards entities such as products, services, issues, organizations, individuals, events, topics, and their attributes. It gives the summarized opinion of a writer or speaker. It has received lot of attention due to increasing number of posts/tweets on social sites. The proposed system is meant to classify a given text of review into positive, negative, or the neutral category. Primary objective of this article is to provide a method of exploiting permutation and combination and chi values for sentiment analysis of product reviews. Publicly available freely dictionary SentiWordNet 3.0 has been used for review classification. The proposed system is domain independent and context aware. Another objective of the proposed system is to identify the feature specific intensity with which reviewer has expressed his opinion. Effectiveness of the proposed system has been verified through performance matrix and compared with other research work.


2021 ◽  
Vol 319 ◽  
pp. 01064
Author(s):  
Issam Aattouchi ◽  
Saida Elmendili ◽  
Fatna Elmendili

Twitter is a microblogging service where users can send and read short messages of 140 characters called “tweets”. Many healthcare-related unstructured and free-text tweets are shared on Twitter, which is becoming a popular domain for medical research. Sentiment analysis is one of the data mining types that provides an estimate of the direction of personality sentiment analysis in natural language processing. By analyzing text, computational linguistics is used to infer and analyze mental knowledge of the web, social media, and related references. The data reviewed actually quantifies the attitudes or feelings of the global society towards specific goods, people, or thoughts and exposes the contextual duality of the knowledge. Sentiment analysis is used in various sectors such as health care. There is an incredible amount of healthcare information available online, such as social media, and websites focused on rating medical problems, that is not accessed in a methodical way. Sentiment analysis has many benefits, such as using medical information to achieve the best possible patient outcome and improve the quality of health care. This review paper focuses on the presented sentiment analysis methods that are used in the medical field.


Webology ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. 389-405
Author(s):  
Rahmad Agus Dwianto ◽  
Achmad Nurmandi ◽  
Salahudin Salahudin

As Covid-19 spreads to other nations and governments attempt to minimize its effect by introducing countermeasures, individuals have often used social media outlets to share their opinions on the measures themselves, the leaders implementing them, and the ways in which their lives are shifting. Sentiment analysis refers to the application in source materials of natural language processing, computational linguistics, and text analytics to identify and classify subjective opinions. The reason why this research uses a sentiment case study towards Trump and Jokowi's policies is because Jokowi and Trump have similarities in handling Covid-19. Indonesia and the US are still low in the discipline in implementing health protocols. The data collection period was chosen on September 21 - October 21 2020 because during that period, the top 5 trending on Twitter included # covid19, #jokowi, #miglobal, #trump, and #donaldtrump. So, this period is most appropriate for taking data and discussing the handling of Covid-19 by Jokowi and Trump. The result shows both Jokowi and Trump have higher negative sentiments than positive sentiments during the period. Trump had issued a controversial statement regarding the handling of Covid-19. This research is limited to the sentiment generated by the policies conveyed by the US and Indonesian Governments via @jokowi and @realDonaldTrump Twitter Account. The dataset presented in this research is being collected and analyzed using the Brand24, a software-automated sentiment analysis. Further research can increase the scope of the data and increase the timeframe for data collection and develop tools for analyzing sentiment.


2019 ◽  
Vol 34 (4) ◽  
pp. 295-310 ◽  
Author(s):  
Huyen T M Nguyen ◽  
Hung V Nguyen ◽  
Quyen T Ngo ◽  
Luong X Vu ◽  
Vu Mai Tran ◽  
...  

Sentiment analysis is a natural language processing (NLP) task of identifying orextracting the sentiment content of a text unit. This task has become an active research topic since the early 2000s. During the two last editions of the VLSP workshop series, the shared task on Sentiment Analysis (SA) for Vietnamese has been organized in order to provide an objective evaluation measurement about the performance (quality) of sentiment analysis tools, and encouragethe development of Vietnamese sentiment analysis systems, as well as to provide benchmark datasets for this task. The rst campaign in 2016 only focused on the sentiment polarity classication, with a dataset containing reviews of electronic products. The second campaign in 2018 addressed the problem of Aspect Based Sentiment Analysis (ABSA) for Vietnamese, by providing two datasets containing reviews in restaurant and hotel domains. These data are accessible for research purpose via the VLSP website vlsp.org.vn/resources. This paper describes the built datasets as well as the evaluation results of the systems participating to these campaigns.


Vector representations for language have been shown to be useful in a number of Natural Language Processing tasks. In this paper, we aim to investigate the effectiveness of word vector representations for the problem of Sentiment Analysis. In particular, we target three sub-tasks namely sentiment words extraction, polarity of sentiment words detection, and text sentiment prediction. We investigate the effectiveness of vector representations over different text data and evaluate the quality of domain-dependent vectors. Vector representations has been used to compute various vector-based features and conduct systematically experiments to demonstrate their effectiveness. Using simple vector based features can achieve better results for text sentiment analysis of APP.


Biotechnology ◽  
2019 ◽  
pp. 120-139
Author(s):  
Seetharaman Balaji

The largest digital repository of information, the World Wide Web keeps growing exponentially and calls for data mining services to provide tailored web experiences. This chapter discusses the overview of information retrieval, knowledge discovery and data mining. It reviews the different stages of data mining and introduces the wide spread biological databanks, their explosion, integration, data warehousing, information retrieval, text mining, text repositories for biological research publications, domain specific search engines, web mining, biological networks and visualization, ontology and systems biology. This chapter also illustrates some technical jargon with picture analogy for a novice learner to understand the concepts clearly.


Author(s):  
Vinod Kumar Mishra ◽  
Himanshu Tiruwa

Sentiment analysis is a part of computational linguistics concerned with extracting sentiment and emotion from text. It is also considered as a task of natural language processing and data mining. Sentiment analysis mainly concentrate on identifying whether a given text is subjective or objective and if it is subjective, then whether it is negative, positive or neutral. This chapter provide an overview of aspect based sentiment analysis with current and future trend of research on aspect based sentiment analysis. This chapter also provide a aspect based sentiment analysis of online customer reviews of Nokia 6600. To perform aspect based classification we are using lexical approach on eclipse platform which classify the review as a positive, negative or neutral on the basis of features of product. The Sentiwordnet is used as a lexical resource to calculate the overall sentiment score of each sentence, pos tagger is used for part of speech tagging, frequency based method is used for extraction of the aspects/features and used negation handling for improving the accuracy of the system.


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