Opinion Mining of Consumer Reviews Using Deep Neural Networks with Word-Sentiment Associations
Ontology highlight
ABSTRACT:
SUBMITTER: Maglogiannis I
PROVIDER: S-EPMC7256371 | biostudies-literature | 2020 Jan
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:DataStream mining is a challenging task for researchers because of the change in data distribution during classification, known as concept drift. Drift detection algorithms emphasize detecting the drift. The drift detection algorithm needs to be very sensitive to change in data distribution for detecting the maximum number of drifts in the data stream. But highly sensitive drift detectors lead to higher false-positive drift detections. This paper proposed a Drift Detection-based Adaptive Ensemble classifier for sentiment analysis and opinion mining, which uses these false-positive drift detections to benefit and minimize the negative impact of false-positive drift detection signals. The proposed method creates and adds a new classifier to the ensemble whenever a drift happens. A weighting mechanism is implemented, which provides weights to each classifier in the ensemble. The weight of the classifier decides the contribution of each classifier in the final classification results. The experiments are performed using different classification algorithms, and results are evaluated on the accuracy, precision, recall, and F1-measures. The proposed method is also compared with these state-of-the-art methods, OzaBaggingADWINClassifier, Accuracy Weighted Ensemble, Additive Expert Ensemble, Streaming Random Patches, and Adaptive Random Forest Classifier. The results show that the proposed method handles both true positive and false positive drifts efficiently.
| S-EPMC8356659 | biostudies-literature
Project description:MotivationAntibiotic resistance constitutes a major public health crisis, and finding new sources of antimicrobial drugs is crucial to solving it. Bacteriocins, which are bacterially produced antimicrobial peptide products, are candidates for broadening the available choices of antimicrobials. However, the discovery of new bacteriocins by genomic mining is hampered by their sequences' low complexity and high variance, which frustrates sequence similarity-based searches.ResultsHere we use word embeddings of protein sequences to represent bacteriocins, and apply a word embedding method that accounts for amino acid order in protein sequences, to predict novel bacteriocins from protein sequences without using sequence similarity. Our method predicts, with a high probability, six yet unknown putative bacteriocins in Lactobacillus. Generalized, the representation of sequences with word embeddings preserving sequence order information can be applied to peptide and protein classification problems for which sequence similarity cannot be used.Availability and implementationData and source code for this project are freely available at: https://github.com/nafizh/NeuBI.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6581433 | biostudies-literature
Project description:Opinion mining is a well-known problem in natural language processing that has attracted increasing attention in recent years. Existing approaches are mainly limited to the identification of direct opinions and are mostly dedicated to explicit opinions. However, in some domains such as medical, the opinions about an entity are not usually expressed by opinion words directly, but they are expressed indirectly by describing the effect of that entity on other ones. Therefore, ignoring indirect opinions can lead to the loss of valuable information and noticeable decline in overall accuracy of opinion mining systems. In this paper, we first introduce the task of indirect opinion mining. Then, we present a novel approach to construct a knowledge base of indirect opinions, called OpinionKB, which aims to be a resource for automatically classifying people's opinions about drugs. Using our approach, we have extracted 896 quadruples of indirect opinions at a precision of 88.08 percent. Furthermore, experiments on drug reviews demonstrate that our approach can achieve 85.25 percent precision in polarity detection task, and outperforms the state-of-the-art opinion mining methods. We also build a corpus of indirect opinions about drugs, which can be used as a basis for supervised indirect opinion mining. The proposed approach for corpus construction achieves the precision of 88.42 percent.
| S-EPMC4427363 | biostudies-literature
Project description:The current dataset is obtained by text mining of ?-hydroxybutyrate (BHB) supplement products' consumer online reviews. The text data of 71 BHB products' consumer reviews were extracted with the aid of the Web Scraper Chrome extension. Then, a lexicon-based sentiment analysis approach was developed to classify the sentiment or polarity of BHB products' consumer reviews. Both word-level and sentence-level sentiment analyses were conducted to score the analyzed text snippets. In terms of word-level sentiment analysis, word clouds of selected BHB products' reviews were generated to give direct observation, and the statistics of high-frequent sentiment words were listed for comparison. In terms of sentence-level sentiment analysis, two factors such as flavor and package were taken into consideration to map the products' polarity distributions. Besides, the complex analysis provides us with the basic statistics of the analyzed BHB customer reviews data.
| S-EPMC7083782 | biostudies-literature
Project description:Dietary supplements remain a relatively underexplored source for drug repurposing. A systematic approach to soliciting responses from a large consumer population is desirable to speed up innovation. We tested a workflow that mines unexpected benefits of dietary supplements from massive consumer reviews. A (non-exhaustive) list of regular expressions was used to screen over 2 million reviews on health and personal care products. The matched reviews were manually analyzed, and one supplement-disease pair was linked to biological databases for enriching the hypothesized association. The regular expressions found 169 candidate reviews, of which 45.6% described unexpected benefits of certain dietary supplements. The manual analysis showed some of the supplement-disease associations to be novel or in agreement with evidence published later in the literature. The hypothesis enrichment was able to identify meaningful function similarity between the supplement and the disease. The results demonstrated value of the workflow in identifying candidates for supplement repurposing.
| S-EPMC5977600 | biostudies-literature
Project description:Customer reviews are valuable resources containing customer opinions and sentiments toward the product. The reviews are informative but can be quite lengthy or may contain repetitive information calling for opinion summarization systems that retain only the significant opinion information from the review. Abstractive summarization is a form of text summarization that generates a summary mimicking a human-written summary [1]. When pretrained language models are finetuned for abstractive review summarization, there usually occurs a problem known as the 'domain shift', because the source and target domains exhibit data from varying distributions [2]. This issue results in performance degradation of the model at the target end. This paper contributes a data package comprising of an annotated abstractive summarization dataset (annotated_abs_summ) of airline reviews having 500 reviews and abstractive summary pairs, a dataset (review_titles_data) consisting of 7079 reviews and review title pairs for review title generatioon or domain adaptive training [3] to address the domain shift problem for abstractive opinion summarization and, an annotated reviews dataset (annotated_sentiment) for rating-based sentiment classification. All datasets have been collected from the Skytrax Review Portal via web scraping using Python programming language. The datasets have several potential use cases. The abstractive summarization dataset can serve as a benchmark dataset for airline review summarization. The dataset for domain adaptive training can be used as a standalone dataset for review title generation. The dataset for sentiment analysis is multipurpose having columns like user rating and recommendation value, that can be used for statistical analysis like finding correlation between these data items as well as for other Natural Language Processing (NLP) tasks like predicting rating or recommendation value from the customer reviews. The datasets can be extended using various data augmentation techniques [4,5]. Moreover, the datasets are related and can be collectively used to develop a multi-task learning model [6] for better learning efficiency and improved performance.
| S-EPMC10504493 | biostudies-literature
Project description:Motivation:A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and their targets. Deep learning reveals features of vertices of a large network that can be adapted in accommodating the similarity-based solutions to provide a flexible method of drug-target prediction. Results:We propose a similarity-based drug-target prediction method that enhances existing association discovery methods by using a topology-based similarity measure. DeepWalk, a deep learning method, is adopted in this study to calculate the similarities within Linked Tripartite Network (LTN), a heterogeneous network generated from biomedical linked datasets. This proposed method shows promising results for drug-target association prediction: 98.96% AUC ROC score with a 10-fold cross-validation and 99.25% AUC ROC score with a Monte Carlo cross-validation with LTN. By utilizing DeepWalk, we demonstrate that: (i) this method outperforms other existing topology-based similarity computation methods, (ii) the performance is better for tripartite than with bipartite networks and (iii) the measure of similarity using network topology outperforms the ones derived from chemical structure (drugs) or genomic sequence (targets). Our proposed methodology proves to be capable of providing a promising solution for drug-target prediction based on topological similarity with a heterogeneous network, and may be readily re-purposed and adapted in the existing of similarity-based methodologies. Availability and Implementation:The proposed method has been developed in JAVA and it is available, along with the data at the following URL: https://github.com/zongnansu1982/drug-target-prediction . Contact:nazong@ucsd.edu. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC5860112 | biostudies-literature
Project description:BackgroundSingapore's national digital contact-tracing (DCT) tool-TraceTogether-attained an above 70% uptake by December 2020 after a slew of measures. Sentiment analysis can help policymakers to assess public sentiments on the implementation of new policy measures in a short time, but there is a paucity of sentiment analysis studies on the usage of DCT tools.ObjectiveWe sought to understand the public's knowledge of, concerns with, and sentiments on the use of TraceTogether over time and their preferences for the type of TraceTogether tool.MethodsWe conducted a cross-sectional survey at a large public hospital in Singapore after the COVID-19 lockdown, from July 2020 through February 2021. In total, 4097 respondents aged 21-80 years were sampled proportionately by sex and 4 age groups. The open-ended responses were processed and analyzed using natural language processing tools. We manually corrected the language and logic errors and replaced phrases with words available in the syuzhet sentiment library without altering the original meaning of the phrases. The sentiment scores were computed by summing the scores of all the tokens (phrases split into smaller units) in the phrase. Stopwords (prepositions and connectors) were removed, followed by implementing the bag-of-words model to calculate the bigram and trigram occurrence in the data set. Demographic and time filters were applied to segment the responses.ResultsRespondents' knowledge of and concerns with TraceTogether changed from a focus on contact tracing and Bluetooth activation in July-August 2020 to QR code scanning and location check-ins in January-February 2021. Younger males had the highest TraceTogether uptake (24/40, 60%), while older females had the lowest uptake (8/34, 24%) in the first half of July 2020. This trend was reversed in mid-October after the announcement on mandatory TraceTogether check-ins at public venues. Although their TraceTogether uptake increased over time, older females continued to have lower sentiment scores. The mean sentiment scores were the lowest in January 2021 when the media reported that data collected by TraceTogether were used for criminal investigations. Smartphone apps were initially preferred over tokens, but the preference for the type of TraceTogether tool equalized over time as tokens became accessible to the whole population. The sentiments on token-related comments became more positive as the preference for tokens increased.ConclusionsThe public's knowledge of and concerns with the use of a mandatory DCT tool varied with the national regulations and public communications over time with the evolution of the COVID-19 pandemic. Effective communications tailored to subpopulations and greater transparency in data handling will help allay public concerns with data misuse and improve trust in the authorities. Having alternative forms of the DCT tool can increase the uptake of and positive sentiments on DCT.
| S-EPMC8900919 | biostudies-literature
Project description:People use the World Wide Web heavily to share their experiences with entities such as products, services or travel destinations. Texts that provide online feedback through reviews and comments are essential for consumer decisions. These comments create a valuable source that may be used to measure satisfaction related to products or services. Sentiment analysis is the task of identifying opinions expressed in such text fragments. In this work, we develop two methods that combine different types of word vectors to learn and estimate the polarity of reviews. We create average review vectors from word vectors and add weights to these review vectors using word frequencies in positive and negative sensitivity-tagged reviews. We applied the methods to several datasets from different domains used as standard sentiment analysis benchmarks. We ensemble the techniques with each other and existing methods, and we compare them with the approaches in the literature. The results show that the performances of our approaches outperform the state-of-the-art success rates.
| S-EPMC10994307 | biostudies-literature
Project description:Neural networks enjoy widespread success in both research and industry and, with the advent of quantum technology, it is a crucial challenge to design quantum neural networks for fully quantum learning tasks. Here we propose a truly quantum analogue of classical neurons, which form quantum feedforward neural networks capable of universal quantum computation. We describe the efficient training of these networks using the fidelity as a cost function, providing both classical and efficient quantum implementations. Our method allows for fast optimisation with reduced memory requirements: the number of qudits required scales with only the width, allowing deep-network optimisation. We benchmark our proposal for the quantum task of learning an unknown unitary and find remarkable generalisation behaviour and a striking robustness to noisy training data.
| S-EPMC7010779 | biostudies-literature