Tetsuya Nasukawa

IBM (United States)

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i-10 index

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Co-Authors

Hironori Takeuchi

Musashi University

Hiroshi Kanayama

Japan Women's University

Hideo Watanabe

Kyushu University

Shourya Roy

American Express (United States)

Tohru Nagano

Kobe University

L. Venkata Subramaniam

IBM (United States)

Koichi Takeda

Japanese Foundation For Cancer Research

Naohiko Uramoto

IBM Research - Tokyo

Masayasu Muraoka

IBM Research - Tokyo

Risa Nishiyama

Tokyo Medical Center

Cooperative Institutions

IBM Research - Tokyo

IBM (United States)

The University of Tokyo

Tokyo Institute of Technology

Osaka University

Kyoto University

National Institute of Advanced Industrial Science and Technology

National Institute of Informatics

The Graduate University for Advanced Studies, SOKENDAI

Carnegie Mellon University

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Research Field

Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques

Junbo Yi Tetsuya Nasukawa Rǎzvan Bunescu W. Niblack

We present sentiment analyzer (SA) that extracts sentiment (or opinion) about a subject from online text documents. Instead of classifying the sentiment of an entire document about a subject, SA detects all references to the given subject, and determines sentiment in each of the references using natural language processing (NLP) techniques. Our sentiment analysis consists of 1) a topic specific feature term extraction, 2) sentiment extraction, and 3) (subject, sentiment) association by relationship analysis. SA utilizes two linguistic resources for the analysis: the sentiment lexicon and the sentiment pattern database. The performance of the algorithms was verified on online product review articles ("digital camera" and "music" reviews), and more general documents including general Webpages and news articles.

Sentiment Analysis

10.1109/icdm.2003.1250949

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Citations (685)

Text Mining of Business-Oriented Conversations at a Call Center

Studies in big data (2014)

Hironori Takeuchi Tetsuya Nasukawa Hideo Watanabe

Viewpoints

Sample (material)

Call centre

Center (category theory)

10.1007/978-3-642-45252-9_8

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Citations (5)

Text analysis and knowledge mining system

IBM Systems Journal (2001)

Tetsuya Nasukawa Tohru Nagano

Large text databases potentially contain a great wealth of knowledge. However, text represents factual information (and information about the author's communicative intentions) in a complex, rich, and opaque manner. Consequently, unlike numerical and fixed field data, it cannot be analyzed by standard statistical data mining methods. Relying on human analysis results in either huge workloads or the analysis of only a tiny fraction of the database. We are working on text mining technology to extract knowledge from very large amounts of textual data. Unlike information retrieval technology that allows a user to select documents that meet the user's requirements and interests, or document clustering technology that organizes documents, we focus on finding valuable patterns and rules in text that indicate trends and significant features about specific topics. By applying our prototype system named TAKMI (Text Analysis and Knowledge MIning) to textual databases in PC help centers, we can automatically detect product failures; determine issues that have led to rapid increases in the number of calls and their underlying reasons; and analyze help center productivity and changes in customers' behavior involving a particular product, without reading any of the text. We have verified that our framework is also effective for other data such as patent documents.

10.1147/sj.404.0967

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Citations (217)

Personality Estimation from Japanese Text.

International Conference on Computational Linguistics (2016)

Koichi Kamijo Tetsuya Nasukawa Hideya Kitamura

We created a model to estimate personality trait from authors’ text written in Japanese and measured its performance by conducting surveys and analyzing the Twitter data of 1,630 users. We used the Big Five personality traits for personality trait estimation. Our approach is a combination of category- and Word2Vec-based approaches. For the category-based element, we added several unique Japanese categories along with the ones regularly used in the English model, and for the Word2Vec-based element, we used a model called GloVe. We found that some of the newly added categories have a stronger correlation with personality traits than other categories do and that the combination of the category- and Word2Vec-based approaches improves the accuracy of the personality trait estimation compared with the case of using just one of them.

Word2vec

Trait

16PF Questionnaire

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Citations (5)

Tweeting about the tsunami?

Akiko Murakami Tetsuya Nasukawa

On 11th March 2011, a 9.0-magnitude megathrust earthquake occurred in the ocean near Japan. This was the first large-scale natural disaster in Japan since the broad adoption of social media tools (such as Facebook and Twitter). In particular, Twitter is suitable for broadcasting information, naturally making it the most frequently used social medias when disasters strike. This paper presents a topical analysis using text mining tools and shows the tools' effectiveness for the analysis of social media data analysis after a disaster. Though an ad hoc system without prepared resources was useful, an improved system with some syntactic pattern dictionaries showed better results.

10.1145/2187980.2188187

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Citations (27)

Getting insights from the voices of customers: Conversation mining at a contact center

Information Sciences (2008)

Hironori Takeuchi L. Venkata Subramaniam Tetsuya Nasukawa Shourya Roy

Center (category theory)

10.1016/j.ins.2008.11.026

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Citations (32)

Robust Method of Pronoun Resolution Using Full-Text Information

International Conference on Computational Linguistics (1994)

Tetsuya Nasukawa

A consistent text contains rich information for resolving ambiguities within its sentences. Even simple syntactic information such as word occurrence and collocation patterns, which can be extracted from the text without deep discourse analysis, improves the accuracy of sentence analysis. Pronoun resolution is a typical proceeding that utilizes this information. Through the use of this information, along with information on the syntactic position of each candidate, 93.8% of pronoun references were resolved correctly in an experiment on computer manuals.

Collocation (remote sensing)

Subject pronoun

Object pronoun

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Challenge to the Psychology in the Era of Data

The Proceedings of the Annual Convention of the Japanese Psychological Association (2017)

Etsuko T. Harada Masami Ikeda Ryota Kobayashi Tetsuya Nasukawa Asako Miura

10.4992/pacjpa.81.0_jpas-011

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A Simple Yet Strong Domain-Agnostic De-bias Method for Zero-Shot Sentiment Classification

Findings of the Association for Computational Linguistics: ACL 2022 (2023)

Yang Zhao Tetsuya Nasukawa Masayasu Muraoka Bishwaranjan Bhattacharjee

Zero-shot prompt-based learning has made much progress in sentiment analysis, and considerable effort has been dedicated to designing high-performing prompt templates. However, two problems exist; First, large language models are often biased to their pre-training data, leading to poor performance in prompt templates that models have rarely seen. Second, in order to adapt to different domains, re-designing prompt templates is usually required, which is time-consuming and inefficient. To remedy both shortcomings, we propose a simple yet strong data construction method to de-bias a given prompt template, yielding a large performance improvement in sentiment analysis tasks across different domains, pre-trained language models, and prompt templates. Also, we demonstrate the advantage of using domain-agnostic generic responses over the in-domain ground-truth data.

Template

Zero (linguistics)

Sentiment Analysis

10.18653/v1/2023.findings-acl.242

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Citations (0)

Visual Concept Naming: Discovering Well-Recognized Textual Expressions of Visual Concepts

Masayasu Muraoka Tetsuya Nasukawa Rudy Raymond Bishwaranjan Bhattacharjee

We propose a task called Visual Concept Naming to associate visual concepts with the corresponding textual expressions, i.e., names of visual concepts found in real-world multimodal data. To tackle the task, we create a dataset consisting of 3.4 million tweets in total in three languages. We also propose a method for extracting candidate names of visual concepts and validating them by exploiting Web-based knowledge obtained through image search. To demonstrate the capability of our method, we conduct an experiment with the dataset we create and evaluate names obtained by our method through crowdsourcing, where we establish an evaluation method to verify the names. The experimental results indicate that the proposed method can identify a wide variety of names of visual concepts. The names we obtained also show interesting insights regarding languages and countries where the languages are used.1

Crowdsourcing

10.1145/3366423.3380006

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Citations (1)