ArticleData in brief2026
BanglaEcomReviewCorpus: A dataset for e-commerce product review sentiment analysis.
Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Online shopping has become an integral part of modern life, connecting consumers to a wide array of products and services. Customer feedback plays a crucial role in shaping business strategies, enhancing service quality, and driving product innovation, making it essential for understanding consumer behaviour and preferences. For analysing those feedbacks, a dataset is Collected from popular websites such as Daraz, Bikroy.com, Picabbo, Shajgoj, and others, this dataset comprises 8685 labeled items reflecting diverse customer feedback. Sentiment categories include 3012 positive, 2881 negative, and 2792 neutral sentences, offering balanced representation for fair sentiment analysis. This dataset is ideal for natural language processing (NLP) tasks, enabling advanced sentiment analysis and exploration of consumer behaviour. It incorporates a range of statistical studies, including summary statistics, histograms, and linguistic patterns such as unigrams, bigrams, and trigrams. Additionally, visualizations like word clouds provide insights into the dataset's structure and linguistic diversity. To ensure data integrity, rigorous collection methods, anonymization, and preprocessing techniques were employed. This publicly available dataset serves as a valuable resource for advancing sentiment analysis, improving business strategies, and supporting interdisciplinary research. It enables insights into customer behavior, aids product and service development, and can be used in both academic teaching and AI training. By capturing feedback from diverse e-commerce platforms, the dataset fosters collaboration across fields such as sociology, linguistics, and psychology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.