GTI at SemEval-2016 Task 5: SVM and CRF for Aspect Detection and Unsupervised Aspect-Based Sentiment Analysis

Álvarez-López, Tamara; Juncal-Martínez, Jonathan; Fernández-Gavilanes, Milagros; Costa-Montenegro, Enrique; González-Castaño, Francisco J.

doi:10.18653/v1/s16-1049

Cited by 28 publications

(21 citation statements)

References 7 publications

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“…Similarly, we adopt F1-score and accuracy for the aspect term extraction and aspect sentiment classification in Hindi. In Table 3 (Pontiki et al, 2016) Baseline (Pontiki et al, 2016) 44.0 51.9 45.4 50.6 --76.4 77.7 67.4 69.3 --NLANGP (Toh and Su, 2016) 72 -López et al, 2016) 66.5 68.5 † ----69.9 -----IIT-TUDA (Kumar et al, 2016) 42.6 64.3 66.6 † 56.9 † --86.7 83.5 † 72.2 76.9 --XRCE (Brun et al, 2016) 61. for each language/problem pair. In aspect extraction problem, architecture A4 yields the best F1-score for Spanish (73.0%), German (24.0%), English (64.9%) and Hindi (53.5%), whereas for French and Dutch we obtain the best F1-score with architectures A2 (67.8%) and A3 (65.7%), respectively.…”

Section: Resultsmentioning

confidence: 99%

Language-Agnostic Model for Aspect-Based Sentiment Analysis

Akhtar¹,

Abhishek²,

Ekbal³

et al. 2019

Proceedings of the 13th International Conference on Computational Semantics - Long Papers

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In this paper, we propose a language-agnostic deep neural network architecture for aspect-based sentiment analysis. The proposed approach is based on Bidirectional Long Short-Term Memory (Bi-LSTM) network, which is further assisted with extra hand-crafted features. We define three different architectures for the successful combination of word embeddings and hand-crafted features. We evaluate the proposed approach for six languages (i.e. English, Spanish, French, Dutch, German and Hindi) and two problems (i.e. aspect term extraction and aspect sentiment classification). Experiments show that the proposed model attains state-of-the-art performance in most of the settings.

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Section: Resultsmentioning

confidence: 99%

Language-Agnostic Model for Aspect-Based Sentiment Analysis

Akhtar¹,

Abhishek²,

Ekbal³

et al. 2019

Proceedings of the 13th International Conference on Computational Semantics - Long Papers

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“…The extension of LDA with modified LDA has been proposed in [36]. The sentiment analysis by using the machine learning, rule based, Lexicon based and deep learning was proposed in [40][41][42][43][44][45][46][47].…”

Section: Related Workmentioning

confidence: 99%

Classification of Social Media Users Based on Disagreement and Stance Analysis

Riaz

Minhas

Bibi³

et al. 2020

Communications in Computer and Information Science

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Analyzing conversational behavior is a primary task in the field of sentiment analysis. Different researchers have proposed their models to perform the sentiment analysis of social media discussions. Existing approaches mainly studying the conversational behavior based on the text in the conversation. In any discussion, the users could have different viewpoints about the topic of conversation. A user can agree or disagree on the topic of discussion. Agreement of a user to the topic is called a stance, and if the user disagrees to the topic, we refer it as disagreement. The classification of the users based on stance and disagreement is not a well-researched area, and need to be explored further. In this work, we have proposed a computational model to classify the members according to stance or disagreement. The proposed model uses the novel approach, and it is a hybrid of topic modeling and VADER (Valence Aware Dictionary and sEntiment Reasoner). To evaluate the proposed model, we have conducted an experiment, and we did use the WhatsApp group discussion, Facebook comments as a dataset. We also compared the proposed model with two baseline approach, topic modeling, and VADER. From the results, we can conclude that the proposed model can effectively classify social media users based on disagreement and stance. Keywords: Opinion mining Á Sentiment analysis Á Stance analysis Á Topic modeling Á LDA Á VADER

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“…• Step 6. After that, each review was broken into tokens and Part-of-speech tagged by using Pyvi library 1 .…”

Section: Preprocessingmentioning

confidence: 99%

“…However, the most common problem in SA is sentence-level sentiment classification in which each sentence is assigned to one of three classes: positive, negative or neutral. This information is enough for many applications, but it is not sufficient when we need to analyze the text in a deeper way [1]. For example, in reviews about the restaurant, customers rarely express their opinion towards the entity as a whole but refer to its specific aspects.…”

Section: Introductionmentioning

confidence: 99%

A Transformation Method for Aspect-Based Sentiment Analysis

Dang

Nguyen²,

Kiet³

et al. 2019

JCC

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Along with the explosion of user reviews on the Internet, sentiment analysis has becomeone of the trending research topics in the field of natural language processing. In the last five years,many shared tasks were organized to keep track of the progress of sentiment analysis for various lan-guages. In the Fifth International Workshop on Vietnamese Language and Speech Processing (VLSP2018), the Sentiment Analysis shared task was the first evaluation campaign for the Vietnamese lan-guage. In this paper, we describe our system for this shared task. We employ a supervised learningmethod based on the Support Vector Machine classifiers combined with a variety of features. Weobtained the F1-score of 61% for both domains, which was ranked highest in the shared task. For theaspect detection subtask, our method achieved 77% and 69% in F1-score for the restaurant domainand the hotel domain respectively.

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GTI at SemEval-2016 Task 5: SVM and CRF for Aspect Detection and Unsupervised Aspect-Based Sentiment Analysis

Cited by 28 publications

References 7 publications

Language-Agnostic Model for Aspect-Based Sentiment Analysis

Language-Agnostic Model for Aspect-Based Sentiment Analysis

Classification of Social Media Users Based on Disagreement and Stance Analysis

A Transformation Method for Aspect-Based Sentiment Analysis

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