Multi-Agent Aspect Level Sentiment Analysis in CRM Systems

Rotovei, Doru

doi:10.1109/synasc.2016.068

Cited by 4 publications

(2 citation statements)

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“…The model has two starting points, one for speaker discrimination and the other for speech recognition. In [13] the discussion is based on the sentiment analysis for the customer relation management where the idea of have a customer loyal for a longer run after their first purchase is very important for a company and their products. The proposed concept in [14] is to use multiaspect level sentiment analysis (MALSA) model in a CRM which not only helps find the sentiment but also has a recommendation approach to recommend the customers throughout the purchase cycle.…”

Section: Introductionmentioning

confidence: 99%

“…This model is entirely based on textual data to find out the sentiments. Rotovei and Negru [15] is an extended study of [13] 655 aspect term extraction that has four approaches like, finding frequent nouns and noun phrases, use of opinion and target relationships, supervised learning and topic modeling. And secondly uses aspect aggregation.…”

Section: Introductionmentioning

confidence: 99%

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Sentimental analysis of audio based customer reviews without textual conversion

Maradithaya,

Katti

2024

IJECE

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The current trends or procedures followed in the customer relation management system (CRM) are based on reviews, mails, and other textual data, gathered in the form of feedback from the customers. Sentiment analysis algorithms are deployed in order to gain polarity results, which can be used to improve customer services. But with evolving technologies, lately reviews or feedbacks are being dominated by audio data. As per literature, the audio contents are being translated to text and sentiments are analyzed using natural processing language techniques. However, these approaches can be time consuming. The proposed work focuses on analyzing the sentiments on the audio data itself without any textual conversion. The basic sentiment analysis polarities are mostly termed as positive, negative, and natural. But the focus is to make use of basic emotions as the base of deciding the polarity. The proposed model uses deep neural network and features such as Mel frequency cepstral coefficients (MFCC), Chroma and Mel Spectrogram on audio-based reviews.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%