A model of textual affect sensing using real-world knowledge

Liu, Hugo; Lieberman, Henry; Selker, Ted

doi:10.1145/604050.604067

Cited by 78 publications

(106 citation statements)

References 0 publications

Supporting

Mentioning

105

Contrasting

Unclassified

Order By: Relevance

“…Several research groups have endeavored to extract emotional content from text automatically (Liu et al 2003, Lu et al 2006). Our approach is to construct an affect lexicon starting from seed lists and expanding the lexicon using the PageRank algorithm on graph structures defined by WordNet and other sources.…”

Section: Motivationmentioning

confidence: 99%

“…LIWC performs keyword spotting of affective processes that includes positive emotions in addition to negative emotions such as anxiety, anger, and sadness. Some investigators have specifically attempted to identify affect in text (Liu et al 2003, Al Masum et al 2007, Alm et al 2005, Aman and Szpakowicz 2008, Abbasi et al 2008) using either an affect lexicon or supervised learning techniques. These efforts have mostly been directed at document-level assessment of affect.…”

Section: Sentiment Analysis and Affective Sensingmentioning

confidence: 99%

“…This research shows the relevance of affect extraction techniques to the objectives of intelligence analysis. Liu, Lieberman, and Selker (2003) recognized the significance of commonsense knowledge in extracting connotative/affective meaning from text. Their approach utilized affective knowledge contained in the Open Mind Common Sense (OMCS) database in the construction of their affect lexicon.…”

Section: Sentiment Analysis and Affective Sensingmentioning

confidence: 99%

“…Our approach is in the spirit of Liu et al (2003), but instead of OMCS, we used ConceptNet -a successor to OMCS. The developers of ConceptNet (Havasi et al 2007, Liu andSingh 2004) structured the sentences in OMCS into a more structured, machine-friendly format.…”

Section: Sentiment Analysis and Affective Sensingmentioning

confidence: 99%

See 3 more Smart Citations

Inferring Group Processes from Computer-Mediated Affective Text Analysis

Schryver¹,

Begoli²,

Jose

et al. 2011

View full text Add to dashboard Cite

Section: Motivationmentioning

confidence: 99%

Section: Sentiment Analysis and Affective Sensingmentioning

confidence: 99%

Section: Sentiment Analysis and Affective Sensingmentioning

confidence: 99%

Section: Sentiment Analysis and Affective Sensingmentioning

confidence: 99%

See 2 more Smart Citations

Inferring Group Processes from Computer-Mediated Affective Text Analysis

Schryver¹,

Begoli²,

Jose

et al. 2011

View full text Add to dashboard Cite

“…Dave et al [12] categorized positive versus negative movie reviews using support vector machines on various types of semantic features based on substitutions and proximity, and achieved an accuracy of at most 88.9% on data from Amazon and Cnn.Net. Liu et al [13] proposed a framework to categorize emotions based on a large dictionary of common sense knowledge and on linguistic models.…”

Section: Related Work On Sentimentsmentioning

confidence: 99%

Markov Blankets and Meta-heuristics Search: Sentiment Extraction from Unstructured Texts

Airoldi

Bai

Padman³

2006

Advances in Web Mining and Web Usage Analysis

View full text Add to dashboard Cite

Abstract. Extracting sentiments from unstructured text has emerged as an important problem in many disciplines. An accurate method would enable us, for example, to mine online opinions from the Internet and learn customers' preferences for economic or marketing research, or for leveraging a strategic advantage. In this paper, we propose a two-stage Bayesian algorithm that is able to capture the dependencies among words, and, at the same time, finds a vocabulary that is efficient for the purpose of extracting sentiments. Experimental results on online movie reviews and online news show that our algorithm is able to select a parsimonious feature set with substantially fewer predictor variables than in the full data set and leads to better predictions about sentiment orientations than several state-of-the-art machine learning methods. Our findings suggest that sentiments are captured by conditional dependence relations among words, rather than by keywords or high-frequency words.

show abstract

Sentiment strength detection in short informal text

Thelwall

Buckley

Paltoglou

et al. 2010

J. Am. Soc. Inf. Sci.

1,376

987

View full text Add to dashboard Cite

A huge number of informal messages are posted every day in social network sites, blogs and discussion forums. Emotions seem to be frequently important in these texts for expressing friendship, showing social support or as part of online arguments. Algorithms to identify sentiment and sentiment strength are needed to help understand the role of emotion in this informal communication and also to identify inappropriate or anomalous affective utterances, potentially associated with threatening behaviour to the self or others. Nevertheless, existing sentiment detection algorithms tend to be commercially-oriented, designed to identify opinions about products rather than user behaviours. This article partly fills this gap with a new algorithm, SentiStrength, to extract sentiment strength from informal English text, using new methods to exploit the de-facto grammars and spelling styles of cyberspace. Applied to MySpace comments and with a lookup table of term sentiment strengths optimised by machine learning, SentiStrength is able to predict positive emotion with 60.6% accuracy and negative emotion with 72.8% accuracy, both based upon strength scales of 1-5. The former, but not the latter, is better than baseline and a wide range of general machine learning approaches.

show abstract

A model of textual affect sensing using real-world knowledge

Cited by 78 publications

References 0 publications

Inferring Group Processes from Computer-Mediated Affective Text Analysis

Inferring Group Processes from Computer-Mediated Affective Text Analysis

Markov Blankets and Meta-heuristics Search: Sentiment Extraction from Unstructured Texts

Sentiment strength detection in short informal text

Contact Info

Product

Resources

About