Academic Publications

@article{math11224647,

title = {Sentiment Difficulty in Aspect-Based Sentiment Analysis},

author = {Adrian-Gabriel Chifu and Sébastien Fournier},

url = {https://www.mdpi.com/2227-7390/11/22/4647},

doi = {10.3390/math11224647},

issn = {2227-7390},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Mathematics},

volume = {11},

number = {22},

abstract = {Subjectivity is a key aspect of natural language understanding, especially in the context of user-generated text and conversational systems based on large language models. Natural language sentences often contain subjective elements, such as opinions and emotions, that make them more nuanced and complex. The level of detail at which the study of the text is performed determines the possible applications of sentiment analysis. The analysis can be done at the document or paragraph level, or, even more granularly, at the aspect level. Many researchers have studied this topic extensively. The field of aspect-based sentiment analysis has numerous data sets and models. In this work, we initiate the discussion around the definition of sentence difficulty in this context of aspect-based sentiment analysis. To assess and quantify the difficulty of the aspect-based sentiment analysis, we conduct an experiment using three data sets: “Laptops”, “Restaurants”, and “MTSC” (Multi-Target-dependent Sentiment Classification), along with 21 learning models from scikit-learn. We also use two textual representations, TF-IDF (Terms frequency-inverse document frequency) and BERT (Bidirectional Encoder Representations from Transformers), to analyze the difficulty faced by these models in performing aspect-based sentiment analysis. Additionally, we compare the models with a fine-tuned version of BERT on the three data sets. We identify the most challenging sentences using a combination of classifiers in order to better understand them. We propose two strategies for defining sentence difficulty. The first strategy is binary and considers sentences as difficult when the classifiers are unable to correctly assign the sentiment polarity. The second strategy uses a six-level difficulty scale based on how many of the top five best-performing classifiers can correctly identify sentiment polarity. These sentences with assigned difficulty classes are then used to create predictive models for early difficulty detection. The purpose of estimating the difficulty of aspect-based sentiment analysis is to enhance performance while minimizing resource usage.},

keywords = {Difficulty, Sentiment Analysis},

pubstate = {published},

tppubtype = {article}

}

@article{badache2022,

title = {Unsupervised and Supervised Methods to Estimate Temporal-Aware Contradictions in Online Course Reviews},

author = {Ismail Badache and Adrian-Gabriel Chifu and Sébastien Fournier},

editor = {MDPI},

url = {https://www.mdpi.com/2227-7390/10/5/809},

doi = {10.3390/math10050809},

year  = {2022},

date = {2022-03-03},

urldate = {2022-03-03},

journal = {Mathematics},

volume = {10},

number = {5},

abstract = {The analysis of user-generated content on the Internet has become increasingly popular for a wide variety of applications. One particular type of content is represented by the user reviews for programs, multimedia, products, and so on. Investigating the opinion contained by reviews may help in following the evolution of the reviewed items and thus in improving their quality. Detecting contradictory opinions in reviews is crucial when evaluating the quality of the respective resource. This article aims to estimate the contradiction intensity (strength) in the context of online courses (MOOC). This estimation was based on review ratings and on sentiment polarity in the comments, with respect to specific aspects, such as “lecturer”, “presentation”, etc. Between course sessions, users stop reviewing, and also, the course contents may evolve. Thus, the reviews are time dependent, and this is why they should be considered grouped by the course sessions. Having this in mind, the contribution of this paper is threefold: (a) defining the notion of subjective contradiction around specific aspects and then estimating its intensity based on sentiment polarity, review ratings, and temporality; (b) developing a dataset to evaluate the contradiction intensity measure, which was annotated based on a user study; (c) comparing our unsupervised method with supervised methods with automatic feature selection, over the dataset. The dataset collected from coursera.org is in English. It includes 2244 courses and 73,873 user-generated reviews of those courses.The results proved that the standard deviation of the ratings, the standard deviation of the polarities, and the number of reviews are suitable features for predicting the contradiction intensity classes. Among the supervised methods, the J48 decision trees algorithm yielded the best performance, compared to the naive Bayes model and the SVM model.},

keywords = {Aspect Detection, Contradiction Intensity, Feature Evaluation, Rating, Sentiment Analysis, Temporality},

pubstate = {published},

tppubtype = {article}

}

@article{Badache2019AFIA,

title = {Prédire l'intensité de contradiction dans les commentaires : faible, forte ou très forte ?},

author = {Ismaïl Badache and Sébastien Fournier and Adrian Chifu},

url = {https://hal.archives-ouvertes.fr/hal-01872267/document},

year  = {2019},

date = {2019-12-01},

journal = {Le Bulletin de l'Association Française pour l'Intelligence Artificielle (AFIA 2019)},

abstract = {Reviews on web resources (e.g. courses, movies) become increasingly exploited in text analysis tasks (e.g. opinion detection, controversy detection). This paper investigates contradiction intensity in reviews exploiting different features such as variation of ratings and variation of polarities around specific entities (e.g. aspects, topics). Firstly, aspects are identified according to the distributions of the emotional terms in the vicinity of the most frequent nouns in the reviews collection. Secondly, the polarity of each review segment containing an aspect is estimated. Only resources containing these aspects with opposite polarities are considered. Finally, some features are evaluated, using feature selection algorithms, to determine their impact on the effectiveness of contradiction intensity detection. The selected features are used to learn some state-of-the-art learning approaches. The experiments are conducted on the Massive Open Online Courses data set containing 2244 courses and their 73,873 reviews, collected from coursera.org. Results showed that variation of ratings, variation of polarities, and reviews quantity are the best predictors of contradiction intensity. Also, J48 was the most effective learning approach for this type of classification.},

keywords = {Aspect Detection, Contradiction Intensity, Criteria Evaluation, Sentiment Analysis},

pubstate = {published},

tppubtype = {article}

}