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Adapting Fleming-Type Learning Style Classifications to Deaf Student Behavior
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In: Sustainability; Volume 14; Issue 8; Pages: 4799 (2022)
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| 2 |
Lexicon‐pointed hybrid N‐gram Features Extraction Model (LeNFEM) for sentence level sentiment analysis
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Abstract:
open access article ; Sentiment analysis of social media textual posts can provide information and knowledge that is applicable in social settings, business intelligence, evaluation of citizens' opinions in governance, and in mood triggered devices in the Internet of Things. Feature extraction and selection is a key determinant of accuracy and computational cost of machine learning models for such analysis. Most feature extraction and selection techniques utilize bag of words, N‐grams, and frequency‐based algorithms especially Term Frequency‐Inverse Document Frequency. However, these approaches do not consider relationships between words, they ignore words' characteristics and they suffer high feature dimensionality. In this paper we propose and evaluate a feature extraction and selection approach that utilizes a fixed hybrid N‐gram window for feature extraction and minimum redundancy maximum relevance feature selection algorithm for sentence level sentiment analysis. The approach improves the existing features extraction techniques, specifically the N‐gram by generating a hybrid vector from words, Part of Speech (POS) tags, and word semantic orientation. The vector is extracted by using a static trigram window identified by a lexicon where a sentiment word appears in a sentence. A blend of the words, POS tags, and the sentiment orientations of the static trigram are used to build the feature vector. The optimal features from the vector are then selected using minimum redundancy maximum relevance (MRMR) algorithm. Experiments were carried out using the public Yelp dataset to compare the performance of the proposed model and existing feature extraction models (BOW, normal N‐grams and lexicon‐based bag of words semantic orientations). Using supervised machine learning classifiers the experimental results showed that the proposed model had the highest F‐measure (88.64%) compared to the highest (83.55%) from baseline approaches. Wilcoxon test carried out ascertained that the proposed approach performed significantly better than the baseline approaches. Comparative performance analysis with other datasets further affirmed that the proposed approach is generalizable.
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Keyword:
feature selection; lexicon; minimum redundancy maximum relevance; N-gram2vec model; sentence level SA; sentiment classification; TF-IDF
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URL: https://doi.org/10.1002/eng2.12374
https://dora.dmu.ac.uk/handle/2086/20632
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| 3 |
Prosody-Based Measures for Automatic Severity Assessment of Dysarthric Speech
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In: Applied Sciences; Volume 10; Issue 19; Pages: 6999 (2020)
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| 4 |
Automatic Irony Detection using Feature Fusion and Ensemble Classifier
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| 5 |
Using deep learning methods for supervised speech enhancement in noisy and reverberant environments
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| 6 |
Mutual information estimation and its applications to machine learning ...
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Gao, Shuyang. - : University of Southern California Digital Library (USC.DL), 2018
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| 7 |
Necessary Morphological Patches Extraction for Automatic Micro-Expression Recognition
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In: Applied Sciences ; Volume 8 ; Issue 10 (2018)
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| 8 |
Presenting a Labelled Dataset for Real-Time Detection of Abusive User Posts
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In: Conference papers (2017)
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| 9 |
Improving TTS with corpus-specific pronunciation adaptation
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In: Interspeech ; https://hal.inria.fr/hal-01338111 ; Interspeech, Sep 2016, San Francisco, United States (2016)
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| 10 |
Understanding behavioral differences between short and long-term drinking abstainers from social media
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| 11 |
Coronary artery analysis: Computerâ assisted selection of bestâ quality segments in multipleâ phase coronary CT angiography
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| 12 |
Probabilistic Speaker Pronunciation Adaptation for Spontaneous Speech Synthesis Using Linguistic Features
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In: Proceedings of Statistical Language and Speech Processing ; International Conference on Statistical Language and Speech Processing (SLSP) ; https://hal.inria.fr/hal-01181192 ; International Conference on Statistical Language and Speech Processing (SLSP), Nov 2015, Budapest, Hungary. pp.229-241 (2015)
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| 13 |
Classification Of Political Affiliations By Reduced Number Of Features ...
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| 14 |
Classification Of Political Affiliations By Reduced Number Of Features ...
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| 15 |
A Novel Real-Time Speech Summarizer System for the Learning of Sustainability
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In: Sustainability ; Volume 7 ; Issue 4 ; Pages 3885-3899 (2015)
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| 17 |
Models for predicting the inflectional paradigm of Croatian words
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In: Slovenščina 2.0: Empirične, aplikativne in interdisciplinarne raziskave, Vol 1, Iss 2, Pp 1-34 (2013) (2013)
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| 18 |
Perceptually motivated speech recognition and mispronunciation detection
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| 19 |
Whodunnit ? Searching for the Most Important Feature Types Signalling Emotion-Related User States in Speech
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: Elsevier, 2012
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| 20 |
Multilingual Vandalism Detection Using Language-Independent & Ex Post Facto Evidence
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In: Departmental Papers (CIS) (2011)
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