Review on Natural Language Processing (NLP) and Its Toolkits for Opinion Mining and Sentiment Analysis

Solangi, Yasir Ali; Solangi, Zulfiqar Ali; Aarain, Samreen; Abro, Amna; Mallah, Ghulam Ali; Shah, Asadullah

doi:10.1109/icetas.2018.8629198

Cited by 61 publications

(22 citation statements)

References 4 publications

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“…According to Bloomberg, a sudden decline has been observed in the oil and gas industry during March 2020, and its future is still in an unpredictable state. Importantly, the failure of OPEC negotiations is also the reason for the decline in oil prices and the COVID-19 outbreak, which has expanded the greater impact on the energy industry of Pakistan (Solangi et al 2019a). This is a very big impact with which existing theoretical propositions, assumptions, methods and models have not kept the pace.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: It is time to control the worst: testing COVID-19 outbreak, energy consumption and CO2 emission

Iqbal

Bilal

Nurunnabi

et al. 2020

Environ Sci Pollut Res

172

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: It is time to control the worst: testing COVID-19 outbreak, energy consumption and CO2 emission

Iqbal

Bilal

Nurunnabi

et al. 2020

Environ Sci Pollut Res

172

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“…The process of sentiment analysis is carried out to glean the underlying sentiment of a body of text. It is most popularly used by business organisations to analyse customer feedback and queries in order to gather insights on how to better their business strategies [25] [26] [27]. The code used to build the sentiment analyser, however, can be used to classify between non-aphasia and aphasia subjects since the very essence of a sentiment analyser is to classify between the various given labelled classes as per the steps mentioned in the previous subsection and as shown in Figure 2 below.…”

Section: Sentiment Analysismentioning

confidence: 99%

Identification of Aphasia using Natural Language Processing

Rao¹,

S²

2021

JUSST

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Aphasia is a neurological disorder of language that precludes a person’s ability to speak, understand, read or write in any language. By virtue of this disorder being inextricably connected to language, there is a vast potential for the application of Natural Language Processing (NLP) for the diagnosis of the disorder. This paper surveys the automated machine-learning-based classification methodologies followed by an attempt to discuss a potential way in which an NLP-backed methodology could be implemented along with its accompanying challenges. It is seen that the need for standardized technology-based diagnostic solutions necessitates the exploration of such a methodology.

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“…We apply tokenization to separate the requirements into sequences of tokens [29]. In our application, the tokens are simply the single words separated by whitespace.…”

Section: B Natural Language Processing Pipelinementioning

confidence: 99%

Topic Modeling on User Stories using Word Mover's Distance

Gülle¹,

Ford²,

Ebel³

et al. 2020

Preprint

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Requirements elicitation has recently been complemented with crowd-based techniques, which continuously involve large, heterogeneous groups of users who express their feedback through a variety of media. Crowd-based elicitation has great potential for engaging with (potential) users early on but also results in large sets of raw and unstructured feedback. Consolidating and analyzing this feedback is a key challenge for turning it into sensible user requirements. In this paper, we focus on topic modeling as a means to identify topics within a large set of crowd-generated user stories and compare three approaches: (1) a traditional approach based on Latent Dirichlet Allocation, (2) a combination of word embeddings and principal component analysis, and (3) a combination of word embeddings and Word Mover's Distance. We evaluate the approaches on a publicly available set of 2,966 user stories written and categorized by crowd workers. We found that a combination of word embeddings and Word Mover's Distance is most promising. Depending on the word embeddings we use in our approaches, we manage to cluster the user stories in two ways: one that is closer to the original categorization and another that allows new insights into the dataset, e.g. to find potentially new categories. Unfortunately, no measure exists to rate the quality of our results objectively. Still, our findings provide a basis for future work towards analyzing crowd-sourced user stories.

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Review on Natural Language Processing (NLP) and Its Toolkits for Opinion Mining and Sentiment Analysis

Cited by 61 publications

References 4 publications

RETRACTED ARTICLE: It is time to control the worst: testing COVID-19 outbreak, energy consumption and CO2 emission

RETRACTED ARTICLE: It is time to control the worst: testing COVID-19 outbreak, energy consumption and CO2 emission

Identification of Aphasia using Natural Language Processing

Topic Modeling on User Stories using Word Mover's Distance

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