Sentiment Analysis for Automated Email Response System

Abbas, Muhammad Babar; Khan, Mukarram

doi:10.1109/comtech.2019.8737827

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Using lesser emotion categories could also increase accuracy, as observed by [6]. Last but not least, as investigated by [42], other machine learning models like RNN can be evaluated for their performance in detecting emotions in email contents.…”

Section: Model Training and Evaluationmentioning

confidence: 93%

A Comparison of Machine Learning Models to Prioritise Emails using Emotion Analysis for Customer Service Excellence

Chuttur

Parianen

2022

Knowl. Eng. Data Sci.

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There has been little research on machine learning for email prioritization for customer service excellence. To fill this gap, we propose and assess the efficacy of various machine learning techniques for classifying emails into three degrees of priority: high, low, and neutral, based on the emotions inherent in the email content. It is predicted that after emails are classified into those three categories, recipients will be able to respond to emails more efficiently and provide better customer service. We use the NRC Emotion Lexicon to construct a labeled email dataset of 517,401 messages for our proposal. Following that, we train and test four prominent machine learning models, MNB, SVM, LogR, and RF, and an Ensemble of MNB, LSVC, and RF classifiers, on the labeled dataset. Our main findings suggest that machine learning may be used to classify emails based on their emotional content. However, some models outperform others. During the testing phase, we also discovered that the LogR and LSVC models performed the best, with an accuracy of 72%, while the MNB classifier performed the poorest. Furthermore, classification performance differed depending on whether the dataset was balanced or imbalanced. We conclude that machine learning models that employ emotions for email classification are a promising avenue that should be explored further.

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Section: Model Training and Evaluationmentioning

confidence: 93%

A Comparison of Machine Learning Models to Prioritise Emails using Emotion Analysis for Customer Service Excellence

Chuttur

Parianen

2022

Knowl. Eng. Data Sci.

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“…The process of sentiment analysis can be undertaken through the application of Deep Learning models or by employing traditional techniques. There has been a notable surge in the adoption of Deep Learning models in recent years, primarily due to their capacity to learn features from data and achieve exceptional performance [7,8,9,10]. This diverse range of deep learning models, including Deep Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory networks, and Bidirectional Encoder Representations from Transformers, finds applications across a wide variety of domains.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment Analysis in Social Media: A Case Study of Hike in University School Fees in Selected Nigerian Universities

Aremu,

Muhammad

2024

JIWE

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Faced with escalating operational costs and government disinvestment, Nigerian public universities are implementing tuition fee increases to maintain institutional functionality. This necessary fiscal measure comes in the wake of 2022 industrial action, which exacerbated pre-existing financial strain through extended work stoppages and potentially higher costs associated with resuming activities, while leaving unaddressed the longstanding demands of academics for improved welfare and working conditions. The court-mandated resumption of academic activities without resolution of these core issues further strained university finances, leading to a significant increase in tuition fees. Using VADER, this study investigated social media sentiments related to the increase in university school fees at Usmanu Danfodiyo University, Sokoto, and the University of Maiduguri. The results revealed that students' sentiments regarding the rise in tuition fees at the two universities were largely neutral, with 4.6% positive sentiment, 7.9% negative sentiment, and 87.5% neutral sentiment identified for Usmanu Danfodiyo University, Sokoto. In contrast, the University of Maiduguri had 0% positive sentiment, 19.8% negative sentiment, and 80.2% neutral sentiment. The study recommends seeking feedback through surveys or student leaders and offering scholarships to indigent students to address fee hike concerns at the two universities. While VADER is designed to handle social media textual data, few misclassifications of sentiments were noted and discussed.

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“…The built-in microphone [2] listens for high-decibel acoustic events such as airbag deployment, impact noise, and car horns to detect an accident. Further, A mobile phone can be mounted on the steering wheel [6] to track driving behavior with the use of a magnetometer present inside mobile phones in addition to an accelerometer and gyroscope. With the use of sensors installed around the exterior of the car [26], car crashes were detected, and text alerts were sent to the emergency contacts.…”

Section: IImentioning

confidence: 99%

Mobile Phone Detection and Notification for The Prevention of Car Accidents

Zope¹,

Jain²,

Jain³

et al. 2022

IJRASET

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The use of mobile devices can easily divert a driver's attention away from the road. Dangerous driving, such as texting and driving, can cause havoc in traffic and jeopardize safety. The goal of this project is to develop an accident-avoidance system that can detect the presence of a mobile phone in the driver's hand by installing an in-car camera facing the driver and running a YOLOv3-Tiny algorithm for mobile phone detection. In addition, the model will issue an audio alert to the driver and use face detection algorithms to determine the driver's identity. Twilio APIs are being used to send live messages to car owners about the actions taken using the car's location information.

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Sentiment Analysis for Automated Email Response System

Cited by 4 publications

References 17 publications

A Comparison of Machine Learning Models to Prioritise Emails using Emotion Analysis for Customer Service Excellence

A Comparison of Machine Learning Models to Prioritise Emails using Emotion Analysis for Customer Service Excellence

Sentiment Analysis in Social Media: A Case Study of Hike in University School Fees in Selected Nigerian Universities

Mobile Phone Detection and Notification for The Prevention of Car Accidents

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