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Development of an Entropy-Based Feature Selection Method and Analysis of Online Reviews on Real Estate

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Hiroki Horino, Hirofumi Nonaka, Elisa Claire Alemán Carreón, Toru Hiraoka

Information Retrieval · Submitted 23 Apr 2019

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim As a result, we achieved a 0.69 F-measure and found that the customers are particularly concerned about the facility of apartment, access, and price of an apartment.

how it is stated · asserted 1.00 read the record →

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arXiv ID1904.11797 (version 1)
Pith IDODYWRU3Y, Pith's permanent record of this paper and its review
CitationElisa Claire Alem\'an Carre\'on, Hirofumi Nonaka, Hiroki Horino, Toru Hiraoka. "Development of an Entropy-Based Feature Selection Method and Analysis of Online Reviews on Real Estate." arXiv:1904.11797 (2019). https://pith.science/paper/ODYWRU3Y
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,665 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.07 · human participants 0.16 · controlled comparison 0.03 · randomized assignment 0.01 · states quantitative result 0.99 · compares with prior results 0.05 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

In recent years, data posted about real estate on the Internet is currently increasing. In this study, in order to analyze user needs for real estate, we focus on "Mansion Community" which is a Japanese bulletin board system (hereinafter referred to as BBS) about Japanese real estate. In our study, extraction of keywords is performed based on the calculation of the entropy value of each word, and we used them as features in a machine learning classifier to analyze 6 million posts at "Mansion Community". As a result, we achieved a 0.69 F-measure and found that the customers are particularly concerned about the facility of apartment, access, and price of an apartment.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 In recent years, data posted about real estate on the Internet is currently increasing. background 1.00 0.04 0.01 asserted 1.00
s1 In this study, in order to analyze user needs for real estate, we focus on "Mansion Community" which is a Japanese bulletin board system (hereinafter referred to as BBS) about Japanese real estate. contribution 0.49 0.71 0.02 asserted 0.94
s2 In our study, extraction of keywords is performed based on the calculation of the entropy value of each word, and we used them as features in a machine learning classifier to analyze 6 million posts at "Mansion Community". method 0.99 0.96 0.04 asserted 0.99
s3 As a result, we achieved a 0.69 F-measure and found that the customers are particularly concerned about the facility of apartment, access, and price of an apartment. result 1.00 0.97 0.84 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.75 · evidence from analysis of existing data 0.96.

What was examined, and what was not

sources read
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  • full text
  • figures and tables
  • references and citing sentences
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  • P abstract
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  • T body sentences
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

In recent years, data posted about real estate on the Internet is currently increasing. In this study, in order to analyze user needs for real estate, we focus on "Mansion Community" which is a Japanese bulletin board system (hereinafter referred to as BBS) about Japanese real estate. In our study, extraction of keywords is performed based on the calculation of the entropy value of each word, and we used them as features in a machine learning classifier to analyze 6 million posts at "Mansion Community". As a result, we achieved a 0.69 F-measure and found that the customers are particularly concerned about the facility of apartment, access, and price of an apartment.

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