A model of textual affect sensing using real-world knowledge

Abstract

This paper presents a novel way for assessing the affective qualities of natural language and a scenario for its use. Previous approaches to textual affect sensing have employed keyword spotting, lexical affinity, statistical methods, and hand-crafted models. This paper demonstrates a new approach, using large-scale real-world knowledge about the inherent affective nature of everyday situations (such as "getting into a car accident") to classify sentences into "basic" emotion categories. This commonsense approach has new robustness implications.Open Mind Commonsense was used as a real world corpus of 400,000 facts about the everyday world. Four linguistic models are combined for robustness as a society of commonsense-based affect recognition. These models cooperate and compete to classify the affect of text.Such a system that analyzes affective qualities sentence by sentence is of practical value when people want to evaluate the text they are writing. As such, the system is tested in an email writing application. The results suggest that the approach is robust enough to enable plausible affective text user interfaces.

A machine learning approach to sentiment analysis in multilingual Web texts

“…For example, the lack of world knowledge mentioned in 6. might be resolved by an approach like the one taken by Liu et al (2003), who use the ''Open Mind Common Sense'' knowledge base to obtain large-scale real-world knowledge about people's common affective attitudes toward situations, things, people, and actions. For more references on some of these problems and their attempted solutions, see Sect.…”

“…Current systems identify the opinion of sentences in documents or of complete documents and classify these as positive, negative or neutral. In some cases other types of sentiment classifications are used (e.g., the emotions ''happy, sad, anger, fear, disgust, surprise''), which reveal other aspects of the content (Huber et al 2000;Turney 2002; Kamps and Marx 2002;Liu et al 2003;Nijholt 2003;Whitelaw et al 2005;Leshed and Kaye 2006). We can distinguish two main techniques for sentiment analysis of texts: symbolic and machine learning techniques.…”

Bidirectional LSTM Networks for Context-Sensitive Keyword Detection in a Cognitive Virtual Agent Framework

“…When applied in human-like cognitive systems, keyword detectors have to process natural and spontaneous speech, which in contrast to well articulated read speech (as used in [6], for example) leads to comparatively low recognition rates [7]. Since modeling emotion and including linguistic models for affect recognition [8,9] plays a major role in the design of cognitive systems [10,1], keyword spotters also need to be robust with respect to emotional coloring of speech. A typical scenario for a cognitive emotionally sensitive virtual agent system that requires keyword detection in emotionally colored speech is the SEMAINE system [4].…”

ML-Ask: Open Source Affect Analysis Software for Textual Input in Japanese

“…For example, Elliott [6] proposed a keyword-based Affect Analysis system applying an affect lexicon (including words like "happy", or "sad") with modifiers (words such as "extremely", or "somewhat"). Liu et al [7] presented a model of text-based affect sensing based on OMCS (Open-Mind Common Sense), a generic common sense database, with an application to e-mail interpretation. Alm et al [8] proposed a machine learning method for Affect Analysis of fairy tales.…”

“…Several affect analysis systems have been proposed till now [7,19,9,10,14,16,41,21,28]. However, none of them has yet been released as an Open Source software.…”