ML-Ask: Affect Analysis System

This page contains information about ML-Ask. ML-Ask is a system for Affect Analysis of textual input in Japanese. It is based on a linguistic assumption that emotional states of a speaker are conveyed by emotional expressions used in emotive utterances. ML-Ask firstly separates emotive utterances from non-emotive and in the emotive utterances seeks for expressions of specific emotion types.

System Description

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.

ML-Ask is based on the idea of two-part classification of realizations of emotions in language into:

1) Emotive elements or emotemes, which indicate that a sentence is emotive, but do not detail what specific emotions have been expressed. For example, interjections such as “whoa!” or “Oh!” indicate that the speaker (producer of the utterance) have conveyed some emotions. However, it is not possible, basing only on the analysis of those words, to estimate precisely what kind of emotion the speaker conveyed.

2) Emotive expressions are words or phrases that directly describe emotional states, but could be used to both express one’s emotions and describe the emotion without emotional engagement.

I collected and hand-crafted a database of 907 emotemes, which include such groups of emotemes as:

interjections: すごい sugoi (great!)

mimetic expressions (gitaigo in Japanese): わくわく wakuwaku (heart pounding)

vulgar language: やがる -yagaru (syntactic morpheme used in verb vulgarization)

emotive sentence markers: ‘!’, or ‘??’ (sentence markers indicating emotiveness)

A set of features similar to what I define as emotemes has been also applied in other research on discrimination between emotive (emotional/subjective) and non-emotive (neutral/objective) sentences (see for example Wiebe et al., 2005, Wilson & Wiebe, 2005, or Aman & Szpakowicz, 2007).
Emotive expressions can be realized by various parts of speech and phrases, such as:

nouns: 愛情　aijou (love),

verbs: 悲しむ　kanashimu (to feel sad, to grieve)

adjectives: 嬉しい　ureshii (happy)

phrases: 虫唾が走る mushizu ga hashiru (to give one the creeps [from hate])

As the collection of emotive expressions ML-Ask uses a database created on the basis of Akira Nakamura’s “Emotive Expression Dictionary”. The emotive expression database is a collection of over two thousand expressions describing emotional states. It also incorporates an emotion classification reflecting Japanese language and cluture. All expressions are classified as representing a specific emotion type, one or more if applicable. In particular, the ten emotion types are: 喜 ki/yorokobi (joy, delight), 怒 dō/ikari (anger), 哀 ai/aware (sorrow, sadness, gloom), 怖 fu/kowagari (fear), 恥 chi/haji (shame, shyness, bashfulness), 好 kō/suki (liking, fondness), 厭 en/iya (dislike, detestation), 昂 kō/takaburi (excitement), 安 an/yasuragi (relief), and 驚 kyō/odoroki (surprise, amazement). The distribution of separate expressions across all emotion classes is represented in the table below.

Emotion class	Nunber of expressions	Emotion class	Nunber of expressions
dislike	532	fondness	197
excitement	269	fear	147
sadness	232	surprise	129
joy	224	relief	106
anger	199	shame	65
		Sum	2100

ML-Ask also implements the idea of Contextual Valence Shifters (CVS) for Japanese. The idea of CVS, as proposed by Polanyi and Zaenen, 2006, assumes two kinds of CVS: negations and intensifiers. Negations are words and phrases like “not”, “never” or “not quite”, which change semantic polarity of an evaluative word they refer to. Intensifiers are words like “very”, “very much” or “deeply”, which intensify the semantic orientation of an evaluative word.
ML-Ask incorporates the negation type of CVS with 108 syntactic negation structures. Examples of CVS negations in Japanese are structures such as:

あまり～ない amari -nai (not quite-)

～とは言えない -to wa ienai (cannot say it is-)

～てはいけない -te wa ikenai (cannot+[verb]-)

As for intensifiers, although ML-Ask does not include them as a separate database, most Japanese intensifiers are included in the emoteme database.

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). An example of negative-activated emotion could be “anger”; a positive-deactivated emotion is, e.g., “relief”. The emotion classes annotated by the system are also generalized on this model.

ML-Ask ONLINE DEMO: soon.