Table 6. Tools for feature extraction from mobile app reviews.
| Tools | Authors | Technique(s) |
|---|---|---|
| SUR-Miner | Gu & Kim (2015) | Classification, Pattern-Based Parsing, NLP Parser, Semantic Dependence Graph (SDG) |
| CLAP | Scalabrino et al. (2019) | Random Forest, Rotation Forest, J48, Simple Cart, SMO, Bayesian Network |
| MARK | Phong et al. (2015) | POS Tags, Word2Vec k-Means Clustering |
| KEFE | Wu et al. (2021) | NLP, ML, Regression Analysis |
| SRR-Miner | Tao, Guo & Huang (2020) | <Misbehavior-aspect-opinion> extraction, POS tag, Bag of Word (BOW) feature, tf-idf, LR |
| SAFE | Johann, Stanik & Maalej (2017) | Coding tool for evaluating feature extraction, implementation of SAFE approach |
| SIRA | Wang et al. (2022a) | BERT+Attr-CRF model for feature extraction |
| User request reference (URR) | Ciurumelea et al. (2017) | NLP Type dependencies (stop words, Porter Stemmer) structure, Text features using Ngrams, Term frequency-based features using TF-IDF |
| SURF | Di Sorbo et al. (2017) | Stanford Typed Dependencies (STD) parser, NLP classifier, Snowball Stemming, stop- word removal |
| T-FREX | Motger et al. (2024a) | Large Language Models |
| CRISTAL | Palomba et al. (2015) | Semi-supervised learning (Expectation Maximization for Naive Bayes (EMNB) from AR-Miner), Lightweight Textual Analysis |
| MAPP-reviews | Alves de Lima, de Araújo & Marcondes Marcacini (2022) | Opinion Mining, Temporal Dynamics of Requirements analysis |
| ARdoc | Panichella et al. (2016) | NLP, TA, SA |
| SAFER | Jiang et al. (2019) | Topic modeling (LDA), feature recommendation algorithm |
| AR-Miner | Chen et al. (2014) | EMNB-LDA, EMNB-ASUM, Stanford Topic Modeling Toolbox, LingPipe |
| IDEA framework | Gao et al. (2018b) | NLP, POS, rule-based methods, Pointwise Mutual Information (PMI) for phrase extraction, Semantic Score, Sentiment Score, AOLDA |
| DIVERSE | Guzman, Alkadhi & Seyff (2016) | Pos tagging, collocation finding algorithm, lexical sentiment analysis (SentiStrength) |
| SOLAR | Gao et al. (2023) | Review helpfulness prediction, Topic modeling, Sentiment analysis, SVM, Random Forest, and EMNB. |
| CASPAR | Guo & Singh (2020) | Natural Language Processing (NLP), Part-of-speech tagging, Dependency parsing |
| GuMa | Guzman & Maalej (2014) | Collocation Finding, Lexical Sentiment Analysis, Topic Modeling |
| ReUS | Dragoni, Federici & Rexha (2019) | Aspect Extraction, Polarity Inference, OpenIE, Sentiment Module |
| RE-SWOT | Dalpiaz & Parente (2019) | NLP, SWOT Analysis |
| PUMA | Vu et al. (2016) | Stanford POS tagger, Phrase Extraction |
| UISMiner | Wang et al. (2022b) | Review Classification, SVM, POS tag, Semantic dependency trees |
| DSISP | Xiao et al. (2020) | Sentiment analysis and NLP (Natural Language Processing) |
| RISING | Zhou et al. (2020) | Semi-supervised clustering, Textual analysis, Feature extraction Stanford NLP toolkit, PCA, BoW, N-gram, VSM, K-means |
| MERIT | Gao et al. (2015a) | Topic Modeling JST, BST, Sentiment Analysis, PMI, BoW, AOBST |
| DIVER | Gao et al. (2019a) | Topic Modeling, Word Collocation Extraction |
| ChangeAdvisor | Palomba et al. (2017) | NLP, ARDOC (Panichella et al., 2015), bag-of-words |
| Oasis | Wei, Liu & Cheung (2017) | Semantics-based similarity |
| INFAR | Gao et al. (2018a) | Feature Extraction |
| AR-TRACER | Gao et al. (2015b) | Topic Modeling |
| PAID | Gao et al. (2015b) | Phrase Extraction, Topic Modeling |
| CrossMiner | Man et al. (2016) | Keyword-based Method Word2Vec |
| Requirements-collector | Panichella & Ruiz (2020) | Machine Learning, Deep Learning |
| CIRA | Martin, Sarro & Harman (2016) | Causal Impact Analysis, Data Mining, Statistical Analysis |
| Automatic UUX | Bakiu & Guzman (2017) | Feature Extraction, Collocation Algorithm, NLTK POS Tagger |
| AOBTM | Hadi & Fard (2023) | AOBTM, Online LDA, Online BTM |
| BECLoMA | Pelloni et al. (2018) | BECLoMA, |
| FeatCompare | Assi et al. (2021) | GLFE (Global-local Sensitive Feature Extractor), TfidfVectorizer, k-means |
| OPT-based approach | de Lima, Barbosa & Marcacini (2023a) | OPT-based approach |
| Mapp-IDEA | de Lima, Barbosa & Marcacini (2023b) | Sentiment Analysis, Word Embedding, BERT |
| STRE | Tan et al. (2023) | Textual Similarity Topics (TST) Extraction using LDA, Review Topic Identification, Learning Algorithm |
| CEAR | Zahoor & Bawany (2023) | Machine Learning (Random Forest, Naive Bayes, SVM, Decision Tree, Logistic Regression, ANN, LSTM, RNN), LIME for model explanations |
| IETI | Zhou et al. (2022) | Natural Language Processing, Adaptive Online Biterm Topic Model, PMI for Phrase Extraction |
| TransFeatEx | Gallego Marfa et al. (2023) | Natural Language Processing, RoBERTa, POS patterns, syntactic dependency patterns |