A hybrid approach to the sentiment analysis problem at the sentence level

Appel, Orestes; Chiclana, Francisco; Carter, Jenny; Fujita, Hamido

doi:10.1016/j.knosys.2016.05.040

Cited by 191 publications

(112 citation statements)

References 44 publications

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“…Sentence-oriented sentiment analysis methods (Socher et al, 2011;Appel et al, 2016) are not capable to capture such fine-grained sentiments on opinion targets.…”

Section: Introductionmentioning

confidence: 99%

Recurrent Attention Network on Memory for Aspect Sentiment Analysis

Chen¹,

Sun²,

Bing

et al. 2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

816

466

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We propose a novel framework based on neural networks to identify the sentiment of opinion targets in a comment/review. Our framework adopts multiple-attention mechanism to capture sentiment features separated by a long distance, so that it is more robust against irrelevant information. The results of multiple attentions are non-linearly combined with a recurrent neural network, which strengthens the expressive power of our model for handling more complications. The weightedmemory mechanism not only helps us avoid the labor-intensive feature engineering work, but also provides a tailor-made memory for different opinion targets of a sentence. We examine the merit of our model on four datasets: two are from SemEval2014, i.e. reviews of restaurants and laptops; a twitter dataset, for testing its performance on social media data; and a Chinese news comment dataset, for testing its language sensitivity. The experimental results show that our model consistently outperforms the state-of-the-art methods on different types of data.

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“…Sentence-oriented sentiment analysis methods (Socher et al, 2011;Appel et al, 2016) are not capable to capture such fine-grained sentiments on opinion targets.…”

Section: Introductionmentioning

confidence: 99%

Recurrent Attention Network on Memory for Aspect Sentiment Analysis

Chen¹,

Sun²,

Bing

et al. 2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

816

466

View full text Add to dashboard Cite

show abstract

“…This approach improves the performance compared with Naïve Bayes and Maximum Entropy state of art techniques. The proposed approach focuses on sentiment analysis at sentence level and in polarity determination [24]. An aspect based extraction in opinion mining and Deep Convolutional Neural Network is used to extract the opinionated words from the sentence either as aspect or not aspect word [25].…”

Section: Hybrid Based Approachesmentioning

confidence: 99%

“…The comparison of hybrid based approach is presented in table 3. A methodology called Sentiment/opinion lexicon, Semantic rules, Fuzzy Sets, HSC, HAC, which achieves better classification accuracy -88.02% and Precision -84.24% compared to that of twitter data set is discussed [24]. The methodology proposed by [28] SWN-Vocabulary, which is performed on LMR data set achieves highest performance accuracy -85.76%, recall -87.47%, F-Measure -86%.…”

Section: Hybrid Based Approachesmentioning

confidence: 99%

Perspectives of the performance metrics in lexicon and hybrid based approaches: a review

Rani¹,

Sumathy²

2017

IJET

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Online social media and social networking services experience a drastic development in the present scenario. Contents generated by hundreds of millions of users are used for communication in general. Users mark their opinion and review in various applications such as Twitter, Facebook, YouTube, Weibo, Flicker, LinkedIn, Online-e commerce sites, Microblogging sites, etc. User generated text is spread rapidly on the web, and it has become tedious to analyze the opinionated text in order to arrive at a decision. Sentiment analysis, a subcategory of text mining is the major active research domain in current era due to greater quantity of opinionated text present in the Internet. Semantic detection is the sub-class in the sentiment analysis which is used for measuring the sentiment orientation in any text. Opinionated text is used for analyzing and making the decision simple. This interdisciplinary field draws various techniques from data mining, machine learning, natural language processing, lexicon based and hybrid based approaches. This paper provides a broad perspective with the highlight of the current state-of art techniques emphasizing the various research challenges and gaps present. The performance metrics in terms of detection rate, precision, recall, f-measure/score, average mean, auto-Pearson correlation, cosine similarity and ratio of time on various algorithms is discussed in detail. An analysis of the text mining approaches in different domains is presented.

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“…positive, negative and neutral. The methods used for sentiment classification are: machine learning approaches [2], [8], lexicon-based approaches [11], [13], [17] and hybrid approaches [18].…”

Section: Related Workmentioning

confidence: 99%

“…Implicit negators are words like "avoid", "deny" etc. The various approaches that are used for polarity shift detection are: machine learning approaches [2], [8], lexicon-based approaches [11], [13], [17] and hybrid approaches [18]. These are widely used along with some rule based approaches and statistical approaches.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Polarity Shift Detection

Mistry

Balani

2017

IJSC

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Now-a-days sentiment analysis has become a hot research area. With the increasing use of internet, people express their views by using social media, blogs, etc. So there is a dire need to analyze people's opinions. Sentiment classification is the main task of sentiment analysis. But while classifying sentiments, the problem of polarity shift occurs. Polarity shift is considered as a very crucial problem. Polarity shift changes a text from positive to negative and vice versa. In this paper, a hybrid approach is proposed for polarity shift detection of negation (explicit and implicit) and contrast. The hybrid approach consists of a rule-based approach for detecting explicit negation and contrast and a lexicon called SentiWordNet for detecting implicit negation. The proposed approach outperforms its baselines.

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A hybrid approach to the sentiment analysis problem at the sentence level

Cited by 191 publications

References 44 publications

Recurrent Attention Network on Memory for Aspect Sentiment Analysis

Recurrent Attention Network on Memory for Aspect Sentiment Analysis

Perspectives of the performance metrics in lexicon and hybrid based approaches: a review

A Hybrid Approach for Polarity Shift Detection

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