Polarity Sentiment-Based Intelligent Chat Bot For Judicious Customer Service Escalation

Prasad, B S; Akana, Chandra Mouli Venkata Srinivas

doi:10.1109/gcat52182.2021.9587592

Cited by 1 publication

(1 citation statement)

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“…The tokenizer you choose should be compatible with the language that your pre-trained model uses. [14] [17] Currently, "Spacy Tokenizer" supports 63 different languages. We have supported English language hence -Whitespace Tokenizer" is used for our Agribot.…”

Section: E Model Pipeline Designmentioning

confidence: 99%

Agribot - Using Rasa Framework

Darapaneni,

Paduri,

Abraham

et al. 2023

Recent Trends in Data Science and Its Applications

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India's economy and society are largely dependent on agriculture, and the nation currently has one of the highest percentages of farm producers in the world. Because they are ignorant of new technologies and criteria that could help them increase productivity, farmers lose production. Farmers require technological assistance and support. From January 2015 to September 2017, all call records from the Kisan Call Center (KCC) were made public by the Indian government. Farmers and agriculture experts provided the researchers with similar KCC datasets. We identified four key areas in need of information support based on both sources: plant protection, pests (diseases), weather, and best practices.Natural language and in-depth learning The use of chatbots has increased significantly in recent years. They are used in many different fields, such as personal assistance, reservation systems, and customer service. To overcome challenges in their fields, farmers continue to rely significantly on the advice of their colleagues. Inadequate usage of these technologies has prevented farmers from receiving the crucial information on time. This project aims to develop "AgriBot," a closed-domain ChatBot for the agricultural industry. Farmers can interact with closed-domain and get expert advice about their industry. The basis for "AgriBot" is the RASA Open-Source Framework. The "AgriBot" deciphers the user's words to determine the issue and entity, then retrieves and distributes the solution from the database.We tested the Bot with existing data and found it to be promising.

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Section: E Model Pipeline Designmentioning

confidence: 99%