An Enhanced Approach to Map Domain-Specific Words in Cross-Domain Sentiment Analysis

Geethapriya, A.; Valli, S.

doi:10.1007/s10796-020-10094-5

Cited by 16 publications

(13 citation statements)

References 59 publications

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“…Given the versatility of internet search trends as an indicator of mental health changes, a popular and successful approach to analyzing such text corpora is using natural language processing (NLP). NLP is a computational-based approach to analyzing text, and it has been used to study and model a variety of mental health constructs [22][23][24][25][26][27]. A particularly useful facet of NLP is sentiment analysis, which is a method of computationally assessing opinions, subjectivity, and emotion in text [22].…”

Section: Introductionmentioning

confidence: 99%

“…Studies leveraging sentiment analysis have also relied on automatic techniques that operate by implementing machine learning-based methods, which classify the sentiment of novel text by "learning" from the features of example data (eg, [24]). In more recent literature, hybrid techniques combining elements of both rule-based and automatic techniques have emerged, often being used for analyzing sentiment across domains (eg, [25,26]). Sentiment analysis has been commonly applied to text from social media platforms such as Twitter.…”

Section: Introductionmentioning

confidence: 99%

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Using the COVID-19 Pandemic to Assess the Influence of News Affect on Online Mental Health-Related Search Behavior Across the United States: Integrated Sentiment Analysis and the Circumplex Model of Affect

Lekkas¹,

Gyorda²,

Price³

et al. 2022

J Med Internet Res

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Background The digital era has ushered in an unprecedented volume of readily accessible information, including news coverage of current events. Research has shown that the sentiment of news articles can evoke emotional responses from readers on a daily basis with specific evidence for increased anxiety and depression in response to coverage of the recent COVID-19 pandemic. Given the primacy and relevance of such information exposure, its daily impact on the mental health of the general population within this modality warrants further nuanced investigation. Objective Using the COVID-19 pandemic as a subject-specific example, this work aimed to profile and examine associations between the dynamics of semantic affect in online local news headlines and same-day online mental health term search behavior over time across the United States. Methods Using COVID-19–related news headlines from a database of online news stories in conjunction with mental health–related online search data from Google Trends, this paper first explored the statistical and qualitative affective properties of state-specific COVID-19 news coverage across the United States from January 23, 2020, to October 22, 2020. The resultant operationalizations and findings from the joint application of dictionary-based sentiment analysis and the circumplex theory of affect informed the construction of subsequent hypothesis-driven mixed effects models. Daily state-specific counts of mental health search queries were regressed on circumplex-derived features of semantic affect, time, and state (as a random effect) to model the associations between the dynamics of news affect and search behavior throughout the pandemic. Search terms were also grouped into depression symptoms, anxiety symptoms, and nonspecific depression and anxiety symptoms to model the broad impact of news coverage on mental health. Results Exploratory efforts revealed patterns in day-to-day news headline affect variation across the first 9 months of the pandemic. In addition, circumplex mapping of the most frequently used words in state-specific headlines uncovered time-agnostic similarities and differences across the United States, including the ubiquitous use of negatively valenced and strongly arousing language. Subsequent mixed effects modeling implicated increased consistency in affective tone (SpinVA β=–.207; P<.001) as predictive of increased depression-related search term activity, with emotional language patterns indicative of affective uncontrollability (FluxA β=.221; P<.001) contributing generally to an increase in online mental health search term frequency. Conclusions This study demonstrated promise in applying the circumplex model of affect to written content and provided a practical example for how circumplex theory can be integrated with sentiment analysis techniques to interrogate mental health–related associations. The findings from pandemic-specific news headlines highlighted arousal, flux, and spin as potentially significant affect-based foci for further study. Future efforts may also benefit from more expansive sentiment analysis approaches to more broadly test the practical application and theoretical capabilities of the circumplex model of affect on text-based data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using the COVID-19 Pandemic to Assess the Influence of News Affect on Online Mental Health-Related Search Behavior Across the United States: Integrated Sentiment Analysis and the Circumplex Model of Affect

Lekkas¹,

Gyorda²,

Price³

et al. 2022

J Med Internet Res

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show abstract

“…Due to the explosion of this data, the internet has become a huge dynamic repository of public views on a large variety of topics or genres (movie reviews, sports reviews, electronic reviews, etc.) [2]. Sentiment classification has become a key enabler of opinion summarization and extraction that automatically categorizes the sentiment in a piece of text on any topic or entity [3].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Cross Domain Sentiment Classification for Urdu Language

Altaf

Jamal

et al. 2022

IEEE Access

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Sentiment analysis is a widely researched area due to its various applications in customer services, brand monitoring, and market research. Automatic sentiment classification is an important but challenging task. Contrary to the English language, sentiment analysis for low-resource languages like Urdu is an under-explored research area. Most of the work on sentiment analysis in the Urdu language is domain-dependent where models are mostly trained and tested on the same dataset on limited domains. However, sentiments in different domains are expressed differently, and manually annotating the datasets for all possible domains is unfeasible. Training a sentiment classifier using annotated data on one domain and testing it on another domain results in poor performance as the terms appearing in the source domain (training data) might not appear in the target (testing data) domain. In this paper, we present a baseline method for cross-domain sentiment analysis in the Urdu language using two different domains. Feature extraction is performed using n-grams and word embedding techniques. Sentiment classification is performed using machine learning and deep learning classifiers. The proposed method achieves an accuracy, precision, recall, and F1 scores of 0.77, 0.83, 0.68, and 0.75, respectively.INDEX TERMS Cross-domain sentiment analysis; deep learning; Urdu language processing; feature engineering

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“…When meaningful features are added to the vocabulary, it will enhance the performance of sentiment classification. 4 The aspect-based sentiment analysis (ABSA) is considered as text analysis that classifies the opinion and determines the sentiments by using aspects. The ABSA classification can identify the sentiment polarity such as negative, positive and neural that predicts the sentiments from one specific aspect.…”

Section: Introductionmentioning

confidence: 99%

“…The mismatching of domain‐specific words in two domains are occur poor accuracy during the classification process. When meaningful features are added to the vocabulary, it will enhance the performance of sentiment classification 4 . The aspect‐based sentiment analysis (ABSA) is considered as text analysis that classifies the opinion and determines the sentiments by using aspects.…”

Section: Introductionmentioning

confidence: 99%

Local search five‐element cycle optimized reLU‐BiLSTM for multilingual aspect‐based text classification

Kumar¹,

Sulochana

2022

Concurrency and Computation

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Aspect-based sentiment analysis has gained wide popularity due to its benefits of text extraction, classification, and ranking the overall sentiments of each feature extracted. However, the aspect-based feature extraction techniques often result in acquiring more number aspects that refer to the same feature which arises the need for aspect-based text classification. Since most of the existing techniques focus on monolingual aspect-based sentimental analysis, we planned to develop a multilingual aspect-based text classification for Indian languages. We perform the multilingual aspect-based text classification on different morphologically rich and complex languages such as Hindi, Tamil, Malayalam, Bengali, Urdu, Telugu, and Sinhalese. To achieve this objective, in this article we present an optimized rectified linear unit (reLU) layer-based bidirectional long short-term memory (reLU-BiLSTM) deep learning tool is developed. The parameters of the reLU-BiLSTM architecture are optimized using the local search-based five-element cycle optimization algorithm (LSFECO) optimization algorithm. Initially, the proposed model preprocesses the multilingual texts obtained from the reviews using different techniques such as tokenization, special character removal, text normalization and so forth. The discrete and categorical features from the different languages are initially extracted by applying the bidirectional encoder representations from transformers (BERT) model which processes the sentences in the text in a layer-by-layer manner. The context learning and word embeddings (aspects) present in the text are identified using different approaches such as word mover's distance, continuous Bag-of-Words (CBOW), and Cosine similarity. The LSFECO optimized reLU-BiLSTM architecture classifies the different aspects present in the embedding document to its corresponding classes (flowers, plants, animals, sports, politics, etc). The efficiency of the proposed methodology is evaluated using the text obtained from different text documents such as semantic relations from Wikipedia, Habeas Corpus (HC) Corpora, Sentiment Lexicons for 81 Languages, IIT Bombay English-Hindi Parallel Corpus, and Indic Languages Multilingual Parallel Corpus. When compared to conventional techniques, the proposed methodology outperforms them in terms of entropy, coverage, purity, processing time, accuracy, F1-score, recall, and precision.

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An Enhanced Approach to Map Domain-Specific Words in Cross-Domain Sentiment Analysis

Cited by 16 publications

References 59 publications

Using the COVID-19 Pandemic to Assess the Influence of News Affect on Online Mental Health-Related Search Behavior Across the United States: Integrated Sentiment Analysis and the Circumplex Model of Affect

Using the COVID-19 Pandemic to Assess the Influence of News Affect on Online Mental Health-Related Search Behavior Across the United States: Integrated Sentiment Analysis and the Circumplex Model of Affect

Deep Learning Based Cross Domain Sentiment Classification for Urdu Language

Local search five‐element cycle optimized reLU‐BiLSTM for multilingual aspect‐based text classification

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