The Impact of Z_score on Twitter Sentiment Analysis

Hamdan, Hussam; Bellot, Patrice; Béchet, Frédéric

doi:10.3115/v1/s14-2113

Cited by 10 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to the sum of the positive scores and the sum of the negative scores from the automatic constructed lexicons. Z Score Z score can distinguish the importance of each term in each class, their performances have been proved in [21]. We assume as in the mentioned work that the term frequencies are following a multi-nomial distribution.…”

Section: Sentiment Lexiconsmentioning

confidence: 99%

Sentiment Lexicon-Based Features for Sentiment Analysis in Short Text

Hamdan¹,

Bellot²,

Béchet³

2015

RCS

Self Cite

View full text Add to dashboard Cite

Sentiment lexicon-based features have proved their performance in recent work concerning sentiment analysis in Twitter. Automatic constructed lexicon features seem to be enough influential to attract the attention. In this paper, we propose a new metric to estimate the word polarity score, called natural entropy (ne), in order to construct a new sentiment lexicon based on Sentiment140 corpus. We derive six features from the new lexicon and show that (ne) metric outperforms the PMI metric which has been used for the same purpose. For evaluation, we build a state-of-the-art system for sentiment analysis in short text using a supervised classifier trained on several groups of features including n-gram, sentiment lexicons, negation, Z score and semantic features. This system has been one of the best systems in both tasks of SemEval-2015: Sentiment Analysis in Twitter and Aspect-Based Sentiment Analysis. We investigate the impact of the lexicon-based features extracted from existing manual and automatic constructed lexicons on the system performance and also the impact of the proposed metric (ne).

show abstract

Section: Sentiment Lexiconsmentioning

confidence: 99%

Sentiment Lexicon-Based Features for Sentiment Analysis in Short Text

Hamdan¹,

Bellot²,

Béchet³

2015

RCS

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this section, the Z-Score measure is used in order to identify the most salient words belonging to the specific classes (Amazon and OpenEdition). Other authors have used the Z-Score as a measurement of the importance of different terms in a dataset [14]. A high Z-Score for a word in a particular dataset, compared to the other, means that it clearly belongs to that specific context.…”

Section: B Lexical/semantic Propertiesmentioning

confidence: 99%

From Genre Classification to Aspect Extraction: New Annotation Schemas for Book Reviews

Álvarez-López¹,

Bellot²,

Fernández-Gavilanes³

et al. 2018

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)

Self Cite

View full text Add to dashboard Cite

In this paper, new schemas for feature categorization in different types of reviews, in the domain of books, are presented, so aspect extraction techniques could be later applied. We deal here with two different types of reviews: formal reviews about scholarly books, written by experts, and informal ones about fiction books, written by readers which are not necessarily highly qualified. Our final goal is to extract the most relevant aspects or features to which any opinion is expressed in these reviews, along with the sentiment associated, for later integrating it to book recommender systems, improving the quality of the recommendations. Throughout this paper, the need for different annotation schemas is proved, by developing a new review classification system, as well as making an analysis at lexical and semantic levels on both kinds of reviews, for finally concluding with the presentation of the new categorization schemas.

show abstract

“…-Word n-grams Features Unigrams, bigrams and 3-grams are extracted for each word in the context without any stemming or stop-word removing, all terms with occurrence less than 3 are removed from the feature space. -Z score Features As described in [Hamdan et al, 2014a], we tested different thresholds for choosing the words which have the highest Z score, a grid search in the interval [-2..5] with step of 0.5 has been done. We found -0.5 is the best one for book reviews.…”

Section: Sentiment Polaritymentioning

confidence: 99%

Sentiment Analysis in Scholarly Book Reviews

Hamdan¹,

Bellot²,

Béchet³

2016

Preprint

Self Cite

View full text Add to dashboard Cite

So far different studies have tackled the sentiment analysis in several domains such as restaurant and movie reviews. But, this problem has not been studied in scholarly book reviews which is different in terms of review style and size. In this paper, we propose to combine different features in order to be presented to a supervised classifiers which extract the opinion target expressions and detect their polarities in scholarly book reviews. We construct a labeled corpus for training and evaluating our methods in French book reviews. We also evaluate them on English restaurant reviews in order to measure their robustness across the domains and languages. The evaluation shows that our methods are enough robust for English restaurant reviews and French book reviews.

show abstract

The Impact of Z_score on Twitter Sentiment Analysis

Cited by 10 publications

References 13 publications

Sentiment Lexicon-Based Features for Sentiment Analysis in Short Text

Sentiment Lexicon-Based Features for Sentiment Analysis in Short Text

From Genre Classification to Aspect Extraction: New Annotation Schemas for Book Reviews

Sentiment Analysis in Scholarly Book Reviews

Contact Info

Product

Resources

About