11:15   Oral Session 5-KZ – Lexical Affect Analysis
Chair: Elisabeth Andre
11:15
25 mins 		SentiFul: Generating a Reliable Lexicon for Sentiment Analysis
Alena Neviarouskaya, Helmut Prendinger, Mitsuru Ishizuka
Abstract: The main drawback of any lexicon-based sentiment analysis system is the lack of scalability. Thus, in this paper, we will describe methods to automatically generate and score a new sentiment lexicon, called SentiFul, and expand it through direct synonymy relations and morphologic modifications with known lexical units. We propose to distinguish four types of affixes (used to derive new words) depending on the role they play with regard to sentiment features: propagating, reversing, intensifying, and weakening.
11:40
25 mins 		Differentiated Semantic Analysis in Lexical Affect Sensing
Alexander Osherenko, Elisabeth André
Abstract: Recently, there has been considerable interest in the recognition of affect from written and spoken language. In this paper, we describe an approach to lexical affect sensing that performs semantic analysis of texts utilizing comprehensive grammatical information. Hereby, the proposed approach differentiates affect of many classes. In addition, this paper reports on obtained results and discusses them.
12:05
25 mins 		Sentence Level Emotion Tagging
Dipankar Das, Sivaji Bandyopadhyay
Abstract: This paper reports the mechanism of sentence level emotion identification based on emotion tagged word level constituents acquired by an automatic classifier applied on the SemEval 2007 Affect Sensing corpus. Basic set of six emotion types, namely, happy, sad, anger, disgust, fear and surprise have been selected for reliable and semi-automatic word level annotation. WordNet Affect lists have been preprocessed using SentiWordNet information for use in the semi-automatic word level emotion annotation process. The Conditional Random Field (CRF) based word level emotion classification has yielded an accuracy of 87.65% on a test set of 250 sentences. Sense based scoring mechanism has been applied for calculating scores of a sentence for each of the six emotion types. Probable sentence level emotion tags have been assigned based on the system produced ordered sense scores. Postprocessing strategies have been adopted for handling negative words in sentence level emotion tagging. The best two emotion tags, with the maximum sense scores, have been assigned to 250 test sentences and an accuracy of 67.2% has been achieved. The sentence level valence has been calculated based on the total sense score of the word level emotion tags. Accuracy, precision and recall are 60.47%, 67.95 and 65.11 respectively for valence identification on 250 test sentences.