Opinion Mining on Newspaper Quotations

Balahur, Alexandra; Steinberger, Ralf; Goot, Erik van der; Pouliquen, Bruno; Kabadjov, Mijail

doi:10.1109/wi-iat.2009.340

Cited by 72 publications

(63 citation statements)

References 13 publications

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“…But when applying Sentiment Analysis to the News domain, Alexandra Balahur (2009) [9] says it is necessary to clearly define the scope of the tasks in three levels. They are definition of the target; separation of good and bad news content from the good and bad sentiment expressed on the target and analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Sentiment Analysis Methods and Identifying Scope of Negation in Newspaper Articles

Padmaja¹,

Fatima²,

Bandu³

2014

ijarai

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Abstract-Automatic detection of linguistic negation in free text is a demanding need for many text processing applications including Sentiment Analysis. Our system uses online news archives from two different resources namely NDTV and The Hindu. While dealing with news articles, we performed three subtasks namely identifying the target; separation of good and bad news content from the good and bad sentiment expressed on the target and analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge. In this paper, our main focus was on evaluating and comparing three sentiment analysis methods (two machine learning based and one lexical based) and also identifying the scope of negation in news articles for two political parties namely BJP and UPA by using three existing methodologies. They were Rest of the Sentence (RoS), Fixed Window Length (FWL) and Dependency Analysis (DA). Among the sentiment methods the best F-measure was SVM with the values 0.688 and 0.657 for BJP and UPA respectively. On the other hand, the F measures for RoS, FWL and DA were 0.58, 0.69 and 0.75 respectively. We observed that DA was performing better than the other two. Among 1675 sentences in the corpus, according to annotator I, 1,137 were positive and 538 were negative whereas according to annotator II, 1,130 were positive and 545 were negative. Further we also identified the score of each sentence and calculated the accuracy on the basis of average score of both the annotators.

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Section: Related Workmentioning

confidence: 99%

Evaluating Sentiment Analysis Methods and Identifying Scope of Negation in Newspaper Articles

Padmaja¹,

Fatima²,

Bandu³

2014

ijarai

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“…These dictionaries can be created manually (Stone et al, 1966) or automatically (Turney, 2002). Examples of lexicons are WordNet Affect (Strapparava and Valitutti, 2004), SentiWordNet (Esuli and Sebastiani, 2006), MicroWNOP (Cerini et al, 2007) or JRC Tonality (Balahur et al, 2009). However, it is very difficult to collect and maintain a universal sentiment lexicon because different words may be used in different domains (Qiu et al, 2009) and some words are domain dependent (Turney, 2002).…”

Section: Related Workmentioning

confidence: 99%

GPLSI: Supervised Sentiment Analysis in Twitter using Skipgrams

Fernández

Gutiérrez

Gómez

et al. 2014

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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In this paper we describe the system submitted for the SemEval 2014 Task 9 (Sentiment Analysis in Twitter) Subtask B. Our contribution consists of a supervised approach using machine learning techniques, which uses the terms in the dataset as features. In this work we do not employ any external knowledge and resources. The novelty of our approach lies in the use of words, ngrams and skipgrams (notadjacent ngrams) as features, and how they are weighted.

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“…Opinion mining from news articles, however, poses its own challenges because in contrast with the product reviews, not all parts of news articles present opinions (Balahur et al, 2013) and thus finding opinionated sentences itself remains a major obstacle. Our work mainly focus on classifying a sentence in a news article as opinionated or factual.…”

Section: Related Workmentioning

confidence: 99%

A Novel Two-stage Framework for Extracting Opinionated Sentences from News Articles

Pujari¹,

Desai²,

Ganguly³

et al. 2014

Proceedings of TextGraphs-9: The Workshop on Graph-Based Methods for Natural Language Processing

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This paper presents a novel two-stage framework to extract opinionated sentences from a given news article. In the first stage, Naïve Bayes classifier by utilizing the local features assigns a score to each sentence -the score signifies the probability of the sentence to be opinionated. In the second stage, we use this prior within the HITS (Hyperlink-Induced Topic Search) schema to exploit the global structure of the article and relation between the sentences. In the HITS schema, the opinionated sentences are treated as Hubs and the facts around these opinions are treated as the Authorities. The algorithm is implemented and evaluated against a set of manually marked data. We show that using HITS significantly improves the precision over the baseline Naïve Bayes classifier. We also argue that the proposed method actually discovers the underlying structure of the article, thus extracting various opinions, grouped with supporting facts as well as other supporting opinions from the article.

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Opinion Mining on Newspaper Quotations

Cited by 72 publications

References 13 publications

Evaluating Sentiment Analysis Methods and Identifying Scope of Negation in Newspaper Articles

Evaluating Sentiment Analysis Methods and Identifying Scope of Negation in Newspaper Articles

GPLSI: Supervised Sentiment Analysis in Twitter using Skipgrams

A Novel Two-stage Framework for Extracting Opinionated Sentences from News Articles

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