[INTRODUCTION]
This is README for ML-Ask, or eMotive eLement and Expression Analysis system, ver. 3.1-4.3
[OVERVIEW]
ML-Ask, or eMotive eLement and Expression Analysis system, is a keyword-based language-dependent system for automatic affect annotation on utterances in Japanese. It uses a two-step procedure: 1. Specifying whether a sentence is emotive, and 2. Recognizing particular emotion types in utterances described as emotive. The database of emotemes was hand-crafted and contains 907 emotemes, which include such groups of emotemes as interjections, mimetic expressions (gitaigo in Japanese), vulgar language, or emotive sentence markers. The emotive expression database is a collection of over two thousand expressions describing emotional states. ML-Ask also implements the idea of Contextual Valence Shifters (CVS) for Japanese with 108 syntactic negation structures. Finally, ML-Ask implements Russell’s two dimensional model of affect. The model assumes that all emotions can be represented in two dimensions: the valence (positive/negative) and activation (activated/deactivated).
[INSTALLATION]
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Unpack the system repository (zipped file).
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Make sure you are using the system under Linux. Performance under Windows and Mac is still not confirmed.
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Install all dependencies:
3.1 The Perl Programming Language (www.perl.org)
3.2 MeCab: Yet Another Part-of-Speech and Morphological Analyzer (http://taku910.github.io/mecab/)
3.3 MeCAB perl binding (http://taku910.github.io/mecab/bindings.html)
3.4 RE2 regex engine (http://search.cpan.org/dist/re-engine-RE2/)
[USAGE]
To use on standard input, launch in command line as: "perl mlask[version_number].pl" or "perl mlask[version_number]-simple.pl" To use on files, launch in command line as: "perl mlask[version_number].pl input_file.txt > output_file.txt" (or similarly with "-simple"). Using -h or -help option will diplay help message and exit the program.
[COPYRIGHTS AND CONTRIBUTIONS]
The system was developed by Michal Ptaszynski ([email protected]), Pawel Dybala, Rafal Rzepka and Kenji Araki.
Particural role of each contributor: Michal Ptaszynski - main developer and a person representative for the system. Pawel Dybala - created most of the code for the first version of ML-Ask (ML-Ask 1.0, also used in ML-Ask 2.0 and ML-Ask 3.0-3.1). Rafal Rzepka - countless conceptual contributions. Kenji Araki - boss of the Araki Lab at Hokkaido Univeristy Japan, in which works on the system has started.
[REFERENCES]
The ML-Ask system is described in detail in papers below. When using ML-Ask please add reference to either of these papers (or both if you like):
Michal Ptaszynski, Pawel Dybala, Rafal Rzepka and Kenji Araki, “Affecting Corpora: Experiments with Automatic Affect Annotation System - A Case Study of the 2channel Forum -”, In Proceedings of The Conference of the Pacific Association for Computational Linguistics (PACLING-09), September 1-4, 2009, Hokkaido University, Sapporo, Japan, pp. 223-228.
Michal Ptaszynski, Pawel Dybala, Wenhan Shi, Rafal Rzepka and Kenji Araki, “A System for Affect Analysis of Utterances in Japanese Supported with Web Mining”, Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, Vol. 21, No. 2 (April), pp. 30-49 (194-213), 2009.
[BUGS AND COMMENTS]
Please report any comments and bugs to: [email protected]