Sentiment Analysis Technologies in AliMe — An Intelligent Assistant for E-commerce

2021 ◽  
pp. 2050016
Author(s):  
Shuangyong Song ◽  
Chao Wang ◽  
Siyang Liu ◽  
Haiqing Chen ◽  
Huan Chen ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Customer Service ◽  
User Satisfaction ◽  
Business Value ◽  
Analysis Framework ◽  
Artificial Intelligent ◽  
Single Task ◽  
Sentiment Detection ◽  
Intelligent Assistant ◽  
Key Techniques

In this paper, we introduce a sentiment analysis framework and its corresponding key techniques used in AliMe, an artificial intelligent (AI) assistant for e-commerce customer service, whose fundamental ability of sentiment analysis provides support for five upper-layer application modules: user sentiment detection, user sentiment comfort, sentimental generative chatting, user service quality control and user satisfaction prediction. Detailed implementation of each module is demonstrated and experiments show our framework not only performs well on each single task but also manifests its competitive business value as a whole.

scholarly journals Twitter Sentiment Analysis using Deep Learning

2020 ◽  
Vol 9 (7) ◽  
pp. 1040-1044
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Customer Service ◽  
Fast Food ◽  
Short Term Memory ◽  
Binary Classification ◽  
Product Reviews ◽  
Learning Techniques ◽  
Fast Food Restaurants ◽  
Sentiment Detection

The whole world is changing rapidly with current innovations, using the Internet, has become a fundamental requirement in people's lives. Nowadays, a massive amount of data made by social networks based on daily user activities. Gathering and analyzing people's opinions are crucial for business applications when they are extracted and analyzed accurately. This data helps the corporations to improve product quality and provide better customer service. But manually analyzing opinions is an impossible task because the content is unorganized. For this reason, we applied sentiment analysis that is the process of extracting and analyzing the unorganized data automatically. The primary steps to perform sentiment analysis include data collection, pre-processing, word embedding, sentiment detection, and classification using deep learning techniques. This work focused on the binary classification of sentiments for three product reviews of fast-food restaurants. Twitter is chosen as the source of data to perform analysis. All tweets were collected automatically by using Tweepy. The experimented dataset divided into half of the positive and half of the negative tweets. In this paper, three deep learning techniques implemented, which are Convolutional Neural Network (CNN), Bi-Directional Long Short-Term Memory (Bi-LSTM), and CNN-Bi-LSTM, The performance of each of them measured and compared in terms of accuracy, precision, recall, and F1 score Finally, Bi-LSTM scored the highest performance in all metrics compared to the two other techniques.

How Does Employee Infusion Use of CRM Systems Drive Customer Satisfaction? Mechanism Differences Between Face-to-Face and Virtual Channels

MIS Quarterly ◽  
2021 ◽  
Vol 45 (2) ◽  
pp. 719-754
Author(s):  
Liwei Chen ◽  
J. J. Po-An Hsieh ◽  
Arun Rai ◽  
Sean Xin Xu
Keyword(s):  
Customer Satisfaction ◽  
Customer Service ◽  
User Satisfaction ◽  
Critical Role ◽  
Customer Relationship ◽  
Service Firms ◽  
Face To Face ◽  
Virtual Channels ◽  
Service Channels ◽  
Expectation Confirmation

To attain customer satisfaction, service firms invest significant resources to implement customer relationship management (CRM) systems to support internal customer service (CS) employees who provide service to external customers in both face-to-face and virtual channels. How CS employees apply sophisticated CRM systems to interact with customers and how the mechanisms through which their CRM usage affects customer satisfaction vary across service channels and bear important implications. We approach these issues by investigating the concept of infusion use, defined as CS employees’ assessment of the extent to which they use a CRM system to its fullest potential to best support their work in the CRM-enabled service interaction context. Drawing on the IS success framework and expectation confirmation theory, we first formulate a baseline model that explains the direct and indirect mechanisms through which CS employees’ infusion use of CRM systems leads to customers’ expectation confirmation, which in turn affects customers’ satisfaction. We then draw on the lenses of media richness and communication adaptation to theorize why these two mechanisms exert differential influence in face-to-face and virtual channels. We test the hypotheses by collecting multiwave data from CS employees, customers, and firm archives of a Fortune 500 telecom service firm. We find that (1) CS employee infusion use can directly contribute to customer expectation confirmation and indirectly do so through CS employees’ satisfaction with the system (i.e., user satisfaction), and (2) the direct mechanism plays a more critical role in the face-to-face channel, whereas the indirect mechanism is more important in the virtual channel. Our findings inform managers of the avenues through which employees’ infusion use promotes CRM-enabled service success across face-to-face and virtual service channels.

Sentiment Analysis as a Service: A Social Media Based Sentiment Analysis Framework

2017 ◽  
Author(s):  
Kashif Ali ◽  
Hai Dong ◽  
Athman Bouguettaya ◽  
Abdelkarim Erradi ◽  
Rachid Hadjidj
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Analysis Framework

scholarly journals Unsupervised Text Feature Extraction for Academic Chatbot using Constrained FP-Growth

2021 ◽  
Vol 14 ◽  
pp. 1-11
Author(s):  
Suraya Alias
Keyword(s):  
Feature Extraction ◽  
Customer Service ◽  
User Satisfaction ◽  
Feature Vector ◽  
Size Reduction ◽  
Growth Technique ◽  
Feature Extraction Method ◽  
Text Feature ◽  
Text Features ◽  
N Gram

In the edge where conversation merely involves online chatting and texting one another, an automated conversational agent is needed to support certain repetitive tasks such as providing FAQs, customer service and product recommendations. One of the key challenges is to identify and discover user’s intention in a social conversation where the focus of our work in the academic domain. Our unsupervised text feature extraction method for Intent Pattern Discovery is developed by applying text features constraints to the FP-Growth technique. The academic corpus was developed using a chat messages dataset where the conversation between students and academicians regarding undergraduate and postgraduate queries were extracted as text features for our model. We experimented with our new Constrained Frequent Intent Pattern (cFIP) model in contrast with the N-gram model in terms of feature-vector size reduction, descriptive intent discovery, and analysis of cFIP Rules. Our findings show significant and descriptive intent patterns was discovered with confidence rules value of 0.9 for cFIP of 3-sequence. We report an average feature-vector size reduction of 76% compared to the Bigram model using both undergraduate and postgraduate conversation datasets. The usability testing results depicted overall user satisfaction average mean score is 4.30 out of 5 in using the Academic chatbot which supported our intent discovery cFIP approach.

scholarly journals A Novel Sentiment Analysis for Amazon Data with TSA based Feature Selection

2021 ◽  
Vol 22 (1) ◽  
pp. 53-66
Author(s):  
D. Anand Joseph Daniel ◽  
M. Janaki Meena
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
User Satisfaction ◽  
Performance Metrics ◽  
Computation Time ◽  
Feature Reduction ◽  
Training Data ◽  
Product Reviews ◽  
Online Product Reviews

Sentiment analysis of online product reviews has become a mainstream way for businesses on e-commerce platforms to promote their products and improve user satisfaction. Hence, it is necessary to construct an automatic sentiment analyser for automatic identification of sentiment polarity of the online product reviews. Traditional lexicon-based approaches used for sentiment analysis suffered from several accuracy issues while machine learning techniques require labelled training data. This paper introduces a hybrid sentiment analysis framework to bond the gap between both machine learning and lexicon-based approaches. A novel tunicate swarm algorithm (TSA) based feature reduction is integrated with the proposed hybrid method to solve the scalability issue that arises due to a large feature set. It reduces the feature set size to 43% without changing the accuracy (93%). Besides, it improves the scalability, reduces the computation time and enhances the overall performance of the proposed framework. From experimental analysis, it can be observed that TSA outperforms existing feature selection techniques such as particle swarm optimization and genetic algorithm. Moreover, the proposed approach is analysed with performance metrics such as recall, precision, F1-score, feature size and computation time.

scholarly journals Contactless Distribution Path Optimization Based on Improved Ant Colony Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Feng Wu
Keyword(s):  
Customer Service ◽  
User Satisfaction ◽  
Ant Colony Algorithm ◽  
Time Window ◽  
Cost Model ◽  
Ant Colony ◽  
Path Optimization ◽  
Traffic Information ◽  
Research Areas ◽  
Improved Ant Colony Algorithm

In the context of the normalization of the epidemic, contactless delivery is becoming one of the most concerned research areas. In the severe epidemic environment, due to the frequent encounter of bayonet temperature measurement, road closure, and other factors, the real-time change frequency of each traffic information is high. In order to improve the efficiency of contactless distribution and enhance user satisfaction, this paper proposes a contactless distribution path optimization algorithm based on improved ant colony algorithm. First of all, the possible traffic factors in the epidemic environment were analyzed, and the cost of each link in the distribution process was modeled. Then, the customer satisfaction is analyzed according to the customer service time window and transformed into a cost model. Finally, the total delivery cost and user satisfaction cost were taken as the optimization objectives, and a new pheromone updating method was adopted and the traditional ant colony algorithm was improved. In the experiment, the effectiveness of the proposed model and algorithm is verified through the simulation optimization and comparative analysis of an example.

UAE e-Learning Sentiment Analysis Framework

2021 ◽  
Author(s):  
Dana Wehbe ◽  
Ahmed Alhammadi ◽  
Hajar Almaskari ◽  
Kholoud Alsereidi ◽  
Heba Ismail
Keyword(s):  
Sentiment Analysis ◽  
Analysis Framework ◽  
E Learning
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