Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Product Market Demand Analysis Using NLP in Banglish Text with Sentiment
  Analysis and Named Entity Recognition   Product market demand analysis plays a significant role for originating
business strategies due to its noticeable impact on the competitive business
field. Furthermore, there are roughly 228 million native Bengali speakers, the
majority of whom use Banglish text to interact with one another on social
media. Consumers are buying and evaluating items on social media with Banglish
text as social media emerges as an online marketplace for entrepreneurs. People
use social media to find preferred smartphone brands and models by sharing
their positive and bad experiences with them. For this reason, our goal is to
gather Banglish text data and use sentiment analysis and named entity
identification to assess Bangladeshi market demand for smartphones in order to
determine the most popular smartphones by gender. We scraped product related
data from social media with instant data scrapers and crawled data from
Wikipedia and other sites for product information with python web scrapers.
Using Python's Pandas and Seaborn libraries, the raw data is filtered using NLP
methods. To train our datasets for named entity recognition, we utilized
Spacey's custom NER model, Amazon Comprehend Custom NER. A tensorflow
sequential model was deployed with parameter tweaking for sentiment analysis.
Meanwhile, we used the Google Cloud Translation API to estimate the gender of
the reviewers using the BanglaLinga library. In this article, we use natural
language processing (NLP) approaches and several machine learning models to
identify the most in-demand items and services in the Bangladeshi market. Our
model has an accuracy of 87.99% in Spacy Custom Named Entity recognition,
95.51% in Amazon Comprehend Custom NER, and 87.02% in the Sequential model for
demand analysis. After Spacy's study, we were able to manage 80% of mistakes
related to misspelled words using a mix of Levenshtein distance and ratio
algorithms.

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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-th
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The categories of this paper are: q-fin
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The categories of this paper are: chem-ph
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The categories of this paper are: solv-int
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-ex
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The categories of this paper are: eess
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The categories of this paper are: econ
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The categories of this paper are: alg-geom
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The categories of this paper are: cond-mat
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The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: math
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The categories of this paper are: nlin
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The categories of this paper are: q-alg