B angla B ook: A Large-scale B angla Dataset for Sentiment Analysis from Book Reviews (2023)

Abstract: The analysis of consumer sentiment, as expressed through reviews, can provide a wealth of insight regarding the quality of a product. While the study of sentiment analysis has been widely explored in many popular languages, relatively less attention has been given to the Bangla language, mostly due to a lack of relevant data and cross-domain adaptability. To address this limitation, we present BanglaBook, a large-scale dataset of Bangla book reviews consisting of 158,065 samples classified into three broad categories: positive, negative, and neutral. We provide a detailed statistical analysis of the dataset and employ a range of machine learning models to establish baselines including SVM, LSTM, and Bangla-BERT. Our findings demonstrate a substantial performance advantage of pre-trained models over models that rely on manually crafted features, emphasizing the necessity for additional training resources in this domain. Additionally, we conduct an in-depth error analysis by examining sentiment unigrams, which may provide insight into common classification errors in under-resourced languages like Bangla. Our codes and data are publicly available at https://github.com/mohsinulkabir14/BanglaBook .

## introduction the resources publicly available for scholarly investigation in the realm of sentiment analysis (sa) for the bangla language are scarce and limited in quantity @xcite @xcite despite its literary gravitas as the 6 th most spoken language foot_0 in the world with approximately 200 million speakers. in the existing literature on bangla text sa, as shown in ## dataset construction in order to create this dataset, we collect a total of 204,659 book reviews from two online bookshops (rokomari foot_1 and wafilife foot_2 ) using a web scraper developed with several python libraries, including beautifulsoup, selenium, pandas, openpyxl, and webdriver, to collect and process the raw data. for the data collection and preparation process of the banglabook dataset, we first compile a list of urls for authors from online bookstores. from there, we procure urls for the books. we meticulously scrape information such as book titles, author names, book categories, review texts, reviewer names, review dates, and ratings by utilizing these book urls. ## statistical analysis ## developing benchmark for banglabook a series of baseline models with combinations of different lexical and semantic features are chosen to evaluate the banglabook dataset. an overview of the models, evaluation metrics, results, and analysis of the experimental results are provided in this section. ## morphology and negation patterns of bangla understanding the morphology and negation patterns of a language holds paramount importance in the realm of sentiment analysis because negation can alter the meaning of words and phrases, thereby affecting the overall sentiment conveyed by a text. we provide a concise yet insightful recapitulation of the topic in the case of bangla accompanied by review samples from our dataset banglabook as the respective examples. from the linguistic typological standpoint, bangla is categorized as a subject-object-verb (sov) language because the subject, object, and verb generally adhere to said order in its sentential structure @xcite . the most common juxtaposition of polarity from positive to negative is the use of ni (িন) as a tensed negative. for example, the bangla language consists of no negative adverbs or pronouns @xcite . this is why the negative element responsible for the reversal of polarity transcends from the word-level to the sentence-level rendering the occurrences of almost all negations in bangla manifest on the syntactic level @xcite . in the cases of double negatives, we see the involvement of lexical negation, a morphological feature that works with negative affixes (prefixes and suffixes) attached to a root word. the prefixes in bangla have two different phonetic variations or allophones depending on whether the prefix precedes a vowel or a consonant. the same is true for prefixes that imbue a negative connotation to a root word, e.g. o (অ) and on (অন্ ). for example, িকন্তু এই বইিট এই অপূ ণর্ তা েঢেক েফেলেছ। translation: but this book has covered up this incompleteness . another negative prefix that precedes a root word to invert its polarity is nir (িনর্ ). for example, ## conclusion this paper introduces banglabook, the largest bangla book review dataset with 158,065 samples, each labeled with 1 of 3 user sentiments. we provide extensive statistical analysis and strong baselines facilitating the utility of the dataset. given its massive size and fine-grained sentiment distribution, banglabook has the potential to alleviate the resource scarcity in bangla language research.