scholarly journals Leveraging Pre-Trained Contextualized Word Embeddings to Enhance Sentiment Classification of Drug Reviews

2021 ◽  
Vol 35 (4) ◽  
pp. 307-314
Author(s):  
Redouane Karsi ◽  
Mounia Zaim ◽  
Jamila El Alami
Keyword(s):  
Sentiment Analysis ◽  
Classification Performance ◽  
Small Sample ◽  
Sentiment Classification ◽  
Training Data ◽  
Svm Classifier ◽  
Word Embeddings ◽  
Classification Problems ◽  
Lexical Resources ◽  
Word Representation

Traditionally, pharmacovigilance data are collected during clinical trials on a small sample of patients and are therefore insufficient to adequately assess drugs. Nowadays, consumers use online drug forums to share their opinions and experiences about medication. These feedbacks, which are widely available on the web, are automatically analyzed to extract relevant information for decision-making. Currently, sentiment analysis methods are being put forward to leverage consumers' opinions and produce useful drug monitoring indicators. However, these methods' effectiveness depends on the quality of word representation, which presents a real challenge because the information contained in user reviews is noisy and very subjective. Over time, several sentiment classification problems use machine learning methods based on the traditional bag of words model, sometimes enhanced with lexical resources. In recent years, word embedding models have significantly improved classification performance due to their ability to capture words' syntactic and semantic properties. Unfortunately, these latter models are weak in sentiment classification tasks because they are unable to encode sentiment information in the word representation. Indeed, two words with opposite polarities can have close word embeddings as they appear together in the same context. To overcome this drawback, some studies have proposed refining pre-trained word embeddings with lexical resources or learning word embeddings using training data. However, these models depend on external resources and are complex to implement. This work proposes a deep contextual word embeddings model called ELMo that inherently captures the sentiment information by providing separate vectors for words with opposite polarities. Different variants of our proposed model are compared with a benchmark of pre-trained word embeddings models using SVM classifier trained on Drug Review Dataset. Experimental results show that ELMo embeddings improve classification performance in sentiment analysis tasks on the pharmaceutical domain.

Shape-restricted support vector machine (SR-SVM): a SVM classifier taking supplementary shape information of input

2021 ◽  
Vol 40 (1) ◽  
pp. 1481-1494
Author(s):  
Geng Deng ◽  
Yaoguo Xie ◽  
Xindong Wang ◽  
Qiang Fu
Keyword(s):  
Support Vector Machine ◽  
Classification Performance ◽  
Research Literature ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Problems ◽  
Active Set ◽  
Shape Information ◽  
Convex Optimization Problem ◽  
Shape Restrictions

Many classification problems contain shape information from input features, such as monotonic, convex, and concave. In this research, we propose a new classifier, called Shape-Restricted Support Vector Machine (SR-SVM), which takes the component-wise shape information to enhance classification accuracy. There exists vast research literature on monotonic classification covering monotonic or ordinal shapes. Our proposed classifier extends to handle convex and concave types of features, and combinations of these types. While standard SVM uses linear separating hyperplanes, our novel SR-SVM essentially constructs non-parametric and nonlinear separating planes subject to component-wise shape restrictions. We formulate SR-SVM classifier as a convex optimization problem and solve it using an active-set algorithm. The approach applies basis function expansions on the input and effectively utilizes the standard SVM solver. We illustrate our methodology using simulation and real world examples, and show that SR-SVM improves the classification performance with additional shape information of input.

scholarly journals Modified framework for sarcasm detection and classification in sentiment analysis

2019 ◽  
Vol 13 (3) ◽  
pp. 1175 ◽  
Author(s):  
Mohd Suhairi Md Suhaimin ◽  
Mohd Hanafi Ahmad Hijazi ◽  
Rayner Alfred ◽  
Frans Coenen
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Classification Performance ◽  
Sentiment Classification ◽  
Support Vector ◽  
Linear Support Vector Machine ◽  
Social Media Data ◽  
Classification Framework ◽  
Media Data ◽  
F Measure

<span>Sentiment analysis is directed at identifying people's opinions, beliefs, views and emotions in the context of the entities and attributes that appear in text. The presence of sarcasm, however, can significantly hamper sentiment analysis. In this paper a sentiment classification framework is presented that incorporates sarcasm detection. The framework was evaluated using a non-linear Support Vector Machine and Malay social media data. The results obtained demonstrated that the proposed sarcasm detection process could successfully detect the presence of sarcasm in that better sentiment classification performance was recorded. A best average F-measure score of 0.905 was recorded using the framework; a significantly better result than when sentiment classification was performed without sarcasm detection.</span>

scholarly journals Meta-Learner for Amharic Sentiment Classification

2021 ◽  
Vol 11 (18) ◽  
pp. 8489
Author(s):  
Girma Neshir ◽  
Andreas Rauber ◽  
Solomon Atnafu
Keyword(s):  
Sentiment Analysis ◽  
Performance Indicator ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Training Data ◽  
Policy Makers ◽  
The Past ◽  
Classification Framework

The emergence of the World Wide Web facilitates the growth of user-generated texts in less-resourced languages. Sentiment analysis of these texts may serve as a key performance indicator of the quality of services delivered by companies and government institutions. The presence of user-generated texts is an opportunity for assisting managers and policy-makers. These texts are used to improve performance and increase the level of customers’ satisfaction. Because of this potential, sentiment analysis has been widely researched in the past few years. A plethora of approaches and tools have been developed—albeit predominantly for well-resourced languages such as English. Resources for less-resourced languages such as, in this paper, Amharic, are much less developed. As a result, it requires cost-effective approaches and massive amounts of annotated training data, calling for different approaches to be applied. This research investigates the performance of a combination of heterogeneous machine learning algorithms (base learners such as SVM, RF, and NB). These models in the framework are fused by a meta-learner (in this case, logistic regression) for Amharic sentiment classification. An annotated corpus is provided for evaluation of the classification framework. The proposed stacked approach applying SMOTE on TF-IDF characters (1,7) grams features has achieved an accuracy of 90%. The overall results of the meta-learner (i.e., stack ensemble) have revealed performance rise over the base learners with TF-IDF character n-grams.

scholarly journals A Statistical Parsing Framework for Sentiment Classification

2015 ◽  
Vol 41 (2) ◽  
pp. 293-336 ◽  
Author(s):  
Li Dong ◽  
Furu Wei ◽  
Shujie Liu ◽  
Ming Zhou ◽  
Ke Xu
Keyword(s):  
Sentiment Analysis ◽  
Sentiment Classification ◽  
Training Data ◽  
Data Sets ◽  
Syntactic Parsing ◽  
Benchmark Data ◽  
Sentence Level ◽  
Statistical Parsing ◽  
Parse Trees ◽  
Context Free

We present a statistical parsing framework for sentence-level sentiment classification in this article. Unlike previous works that use syntactic parsing results for sentiment analysis, we develop a statistical parser to directly analyze the sentiment structure of a sentence. We show that complicated phenomena in sentiment analysis (e.g., negation, intensification, and contrast) can be handled the same way as simple and straightforward sentiment expressions in a unified and probabilistic way. We formulate the sentiment grammar upon Context-Free Grammars (CFGs), and provide a formal description of the sentiment parsing framework. We develop the parsing model to obtain possible sentiment parse trees for a sentence, from which the polarity model is proposed to derive the sentiment strength and polarity, and the ranking model is dedicated to selecting the best sentiment tree. We train the parser directly from examples of sentences annotated only with sentiment polarity labels but without any syntactic annotations or polarity annotations of constituents within sentences. Therefore we can obtain training data easily. In particular, we train a sentiment parser, s.parser, from a large amount of review sentences with users' ratings as rough sentiment polarity labels. Extensive experiments on existing benchmark data sets show significant improvements over baseline sentiment classification approaches.

scholarly journals Addressing Age-Related Bias in Sentiment Analysis

2019 ◽  
Author(s):  
Mark Díaz ◽  
Isaac Johnson ◽  
Amanda Lazar ◽  
Anne Marie Piper ◽  
Darren Gergle
Keyword(s):  
Sentiment Analysis ◽  
Sociodemographic Factors ◽  
Training Data ◽  
Word Embeddings ◽  
Age Bias ◽  
Age Related ◽  
Model Training ◽  
Analysis Models ◽  
Social Biases

Recent studies have identified various forms of bias in language-based models, raising concerns about the risk of propagating social biases against certain groups based on sociodemographic factors (e.g., gender, race, geography). In this study, we analyze the treatment of age-related terms across 15 sentiment analysis models and 10 widely-used GloVe word embeddings and attempt to alleviate bias through a method of processing model training data. Our results show significant age bias is encoded in the outputs of many sentiment analysis algorithms and word embeddings, and we can alleviate this bias by manipulating training data.

scholarly journals Multiple graph fusion based on Riemannian geometry for motor imagery classification

2022 ◽  
Author(s):  
Xiaofeng Xie ◽  
Xiaokun Zou ◽  
Tianyou Yu ◽  
Rongnian Tang ◽  
Yao Hou ◽  
...  
Keyword(s):  
Riemannian Manifold ◽  
Motor Imagery ◽  
Classification Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Problems ◽  
Eeg Signals ◽  
Spatial Covariance ◽  
Bilinear Mapping ◽  
Subject Specific

AbstractIn motor imagery-based brain-computer interfaces (BCIs), the spatial covariance features of electroencephalography (EEG) signals that lie on Riemannian manifolds are used to enhance the classification performance of motor imagery BCIs. However, the problem of subject-specific bandpass frequency selection frequently arises in Riemannian manifold-based methods. In this study, we propose a multiple Riemannian graph fusion (MRGF) model to optimize the subject-specific frequency band for a Riemannian manifold. After constructing multiple Riemannian graphs corresponding to multiple bandpass frequency bands, graph embedding based on bilinear mapping and graph fusion based on mutual information were applied to simultaneously extract the spatial and spectral features of the EEG signals from Riemannian graphs. Furthermore, with a support vector machine (SVM) classifier performed on learned features, we obtained an efficient algorithm, which achieves higher classification performance on various datasets, such as BCI competition IIa and in-house BCI datasets. The proposed methods can also be used in other classification problems with sample data in the form of covariance matrices.

scholarly journals A Machine Learning Method for the Fine-Grained Classification of Green Tea with Geographical Indication Using a MOS-Based Electronic Nose

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 795
Author(s):  
Dongbing Yu ◽  
Yu Gu
Keyword(s):  
Machine Learning ◽  
Green Tea ◽  
Electronic Nose ◽  
Classification Performance ◽  
Small Sample ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrimination Ability ◽  
Fine Grained

Chinese green tea is known for its health-functional properties. There are many green tea categories, which have sub-categories with geographical indications (GTSGI). Several high-quality GTSGI planted in specific areas are labeled as famous GTSGI (FGTSGI) and are expensive. However, the subtle differences between the categories complicate the fine-grained classification of the GTSGI. This study proposes a novel framework consisting of a convolutional neural network backbone (CNN backbone) and a support vector machine classifier (SVM classifier), namely, CNN-SVM for the classification of Maofeng green tea categories (six sub-categories) and Maojian green tea categories (six sub-categories) using electronic nose data. A multi-channel input matrix was constructed for the CNN backbone to extract deep features from different sensor signals. An SVM classifier was employed to improve the classification performance due to its high discrimination ability for small sample sizes. The effectiveness of this framework was verified by comparing it with four other machine learning models (SVM, CNN-Shi, CNN-SVM-Shi, and CNN). The proposed framework had the best performance for classifying the GTSGI and identifying the FGTSGI. The high accuracy and strong robustness of the CNN-SVM show its potential for the fine-grained classification of multiple highly similar teas.

scholarly journals Unseen Word Representation by Aligning Heterogeneous Lexical Semantic Spaces

2019 ◽  
Vol 33 ◽  
pp. 6900-6907 ◽  
Author(s):  
Victor Prokhorov ◽  
Mohammad Taher Pilehvar ◽  
Dimitri Kartsaklis ◽  
Pietro Lio ◽  
Nigel Collier
Keyword(s):  
Training Data ◽  
Lexical Resources ◽  
Rare Word ◽  
Word Similarity ◽  
Performance Improvements ◽  
Space Transformation ◽  
Word Representation ◽  
Consistent Performance ◽  
Cross Lingual

Word embedding techniques heavily rely on the abundance of training data for individual words. Given the Zipfian distribution of words in natural language texts, a large number of words do not usually appear frequently or at all in the training data. In this paper we put forward a technique that exploits the knowledge encoded in lexical resources, such as WordNet, to induce embeddings for unseen words. Our approach adapts graph embedding and cross-lingual vector space transformation techniques in order to merge lexical knowledge encoded in ontologies with that derived from corpus statistics. We show that the approach can provide consistent performance improvements across multiple evaluation benchmarks: in-vitro, on multiple rare word similarity datasets, and invivo, in two downstream text classification tasks.

scholarly journals MODIFIED CORRELATION WEIGHT K-NEAREST NEIGHBOR CLASSIFIER USING TRAINING DATASET CLEANING METHOD

2021 ◽  
Vol 32 (2) ◽  
pp. 20-25
Author(s):  
Efraim Kurniawan Dairo Kette
Keyword(s):  
Nearest Neighbor ◽  
Training Sample ◽  
Classification Performance ◽  
Training Data ◽  
Training Dataset ◽  
Classification Problems ◽  
K Nearest Neighbor ◽  
Neighborhood Structure ◽  
Data Set ◽  
Sample Data

In pattern recognition, the k-Nearest Neighbor (kNN) algorithm is the simplest non-parametric algorithm. Due to its simplicity, the model cases and the quality of the training data itself usually influence kNN algorithm classification performance. Therefore, this article proposes a sparse correlation weight model, combined with the Training Data Set Cleaning (TDC) method by Classification Ability Ranking (CAR) called the CAR classification method based on Coefficient-Weighted kNN (CAR-CWKNN) to improve kNN classifier performance. Correlation weight in Sparse Representation (SR) has been proven can increase classification accuracy. The SR can show the 'neighborhood' structure of the data, which is why it is very suitable for classification based on the Nearest Neighbor. The Classification Ability (CA) function is applied to classify the best training sample data based on rank in the cleaning stage. The Leave One Out (LV1) concept in the CA works by cleaning data that is considered likely to have the wrong classification results from the original training data, thereby reducing the influence of the training sample data quality on the kNN classification performance. The results of experiments with four public UCI data sets related to classification problems show that the CAR-CWKNN method provides better performance in terms of accuracy.

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