Developing a Concept-Level Knowledge Base for Sentiment Analysis in Singlish

Bajpai, Ram; Ho, Danyuan; Wang, Zhaoxia

doi:10.1007/978-3-319-75487-1_27

Cited by 4 publications

(4 citation statements)

References 28 publications

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“…For instance, the factor analysis was used for clarifying structure of personality, and the big five model was proposed as the five component factors of personality (Digman, 1990;McCrae & Costa, 1987). Furthermore, PCA was utilized in text categorization (Lam & Lee, 1999), and MDS was applied to the development of a knowledge base for sentiment analysis on multiword expressions by Bajpai et al (2017). Now, let us consider the data source.…”

Section: Statistical Methods For Analysismentioning

confidence: 99%

A Consideration of Emotional Topology: Representing Psychological Transitions on a Two-Dimensional Map

Aoki¹,

Hirahara²,

Kato³

et al. 2018

PSYCH

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Qualitative verbal and text data such as narrations and descriptions can include additional information about a subject's spiritual response or deep psyche than questionnaires as quantitative data. Therefore, in the previous studies, various analysis methods have been developed to clarify and visualize the subject's mental state based on these data. The present study focuses on the sequential transitions of the visualization results from our previous study. The purpose of this study was to reveal and visualize the transition patterns of the subjects' mental states by analyzing their utterances. The mental changes were expressed with trajectories in two-dimensional space in which the relationship between various emotions was represented with self-organizing maps (SOM) as analyzed in the previous study. The present study demonstrated the modal patterns of mental changes among the subject groups by clustering the subjects. These patterns were visualized on the two-dimensional space composed of psychological evaluation axes, thus the visualization results were interpreted in terms of psychology. It was concluded that the group's tendencies in terms of mental changes can be comprehended by noting on the transition of the mental states. Psychology fields; hence, many researches in terms of it have conducted the development for this sake, so far. The visualization techniques are utilized to observe certain phenomena which are hard to see or that are invisible. Many algorithms of visualization are classified into scientific approaches and informational ones (MacKinlay, 2000). The former includes visual expressions of data which has a physical component, such as natural or biological phenomena, molecular motion, etc. In contrast, the latter visualization type concerns numerical and text data. In this study, we focus on the latter visualization, this is, analysis of text data and its visualization. Previous Studies Text mining with Verbal AnalysisAs for analysis of text data, verbal or text data can have a complex and varied structure, so many kinds of analysis have been developed in text or data mining, e.g., for subjects' narrations and descriptions. For instance, descriptive data in newspaper articles (Yatsuzuka, 2007), diary (Cavicchiolo et al., 2015), and social media posts (Cohn et al., 2004) have all been used in text mining. In addition to data mining processes, verbal data acquired through interviews have also been analyzed by Seale et al. (2006). These data include information regarding the mental states of participants, such as emotions, attitudes, and personalities. Qualitative Data AnalysisRecently, qualitative data such as interview data and free-description data are usually processed with no numerical measurement for statistical methods. This is based on theoretical backgrounds with a focus on the KJ method and grounded theory. For instance, nurses or clinical psychologists with field experience conduct such data processing subjectively, but it is desired to construct theories according to discussion with ob...

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Section: Statistical Methods For Analysismentioning

confidence: 99%

A Consideration of Emotional Topology: Representing Psychological Transitions on a Two-Dimensional Map

Aoki¹,

Hirahara²,

Kato³

et al. 2018

PSYCH

View full text Add to dashboard Cite

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“…Many of the human engineered heuristics used in previous work on ASLG can be largely automated via clever application of new machine learning techniques. Sentiment analysis knowledge bases can be constructed using graph-mining and multi-dimensional scaling techniques (Bajpai et al, 2016 ). Once constructed, these knowledge bases allow for the application of a host of additional methods.…”

Section: Related Workmentioning

confidence: 99%

Augmenting Semantic Lexicons Using Word Embeddings and Transfer Learning

Alshaabi

Oort

Fudolig

et al. 2022

Front. Artif. Intell.

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Sentiment-aware intelligent systems are essential to a wide array of applications. These systems are driven by language models which broadly fall into two paradigms: Lexicon-based and contextual. Although recent contextual models are increasingly dominant, we still see demand for lexicon-based models because of their interpretability and ease of use. For example, lexicon-based models allow researchers to readily determine which words and phrases contribute most to a change in measured sentiment. A challenge for any lexicon-based approach is that the lexicon needs to be routinely expanded with new words and expressions. Here, we propose two models for automatic lexicon expansion. Our first model establishes a baseline employing a simple and shallow neural network initialized with pre-trained word embeddings using a non-contextual approach. Our second model improves upon our baseline, featuring a deep Transformer-based network that brings to bear word definitions to estimate their lexical polarity. Our evaluation shows that both models are able to score new words with a similar accuracy to reviewers from Amazon Mechanical Turk, but at a fraction of the cost.

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“…A speaker's individual style of Creole can vary dramatically depending on social factors, such as their age, ethnicity, geography, and social status. These variations can manifest themselves in different linguistic functions of the Creole, for instance, in the chosen syntax, morphology, or lexical choices (Bajpai et al, 2017). Below, we discuss other features of some (not all) Creoles, that are particularly notable in the context for NLP.…”

Section: Notable Features Of Creolesmentioning

confidence: 99%

What a Creole Wants, What a Creole Needs

Lent¹,

Ogueji²,

Lhoneux³

et al. 2022

Preprint

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In recent years, the natural language processing (NLP) community has given increased attention to the disparity of efforts directed towards high-resource languages over low-resource ones. Efforts to remedy this delta often begin with translations of existing English datasets into other languages. However, this approach ignores that different language communities have different needs. We consider a group of low-resource languages, Creole languages. Creoles are both largely absent from the NLP literature, and also often ignored by society at large due to stigma, despite these languages having sizable and vibrant communities. We demonstrate, through conversations with Creole experts and surveys of Creole-speaking communities, how the things needed from language technology can change dramatically from one language to another, even when the languages are considered to be very similar to each other, as with Creoles. We discuss the prominent themes arising from these conversations, and ultimately demonstrate that useful language technology cannot be built without involving the relevant community.

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Developing a Concept-Level Knowledge Base for Sentiment Analysis in Singlish

Cited by 4 publications

References 28 publications

A Consideration of Emotional Topology: Representing Psychological Transitions on a Two-Dimensional Map

A Consideration of Emotional Topology: Representing Psychological Transitions on a Two-Dimensional Map

Augmenting Semantic Lexicons Using Word Embeddings and Transfer Learning

What a Creole Wants, What a Creole Needs

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