Analyzing Customer Feedback for Web Microservices

In today's world, many customer reviews can be found on various platforms including e-commerce sites, social media, and online services. This article focuses on reviews related to web microservices. A web microservice is a small, self-contained part of an online application that performs a specific task. Microservices are part of a software design approach that breaks applications into smaller, independent services.

Customer reviews act as important feedback about these web microservices. They reflect users' experiences and can significantly impact the reputation of these services. Positive reviews can attract new users, while negative reviews can drive potential customers away. However, reviews can be confusing and not always clear due to various reasons like emotions and word choices used by the reviewer.

To make sense of these reviews, we can use sentiment analysis. This method helps change unreadable reviews into organized data that can be especially useful for managing reputation. Having good reviews is vital for the success of a service as it builds trust among users. The primary aim here is to analyze reviews of web microservices and use that information to assess their reputation.

#What are Web Microservices?

Web microservices are small, specialized components of online applications that are designed to perform specific functions. They represent a modern software design method where independent services can easily connect with one another. This modular approach allows developers to work on different parts of an application without causing disruptions to other components. This also allows for easier updates and maintenance.

The quality of these services, such as response time and availability, is crucial for both users and service providers. One effective way to improve the quality of these services is by analyzing feedback from user reviews. Users often share their experiences, which can indicate how much they like or dislike a microservice.

#Reputation Management

Reputation plays a crucial role in how a service is viewed by its users and potential customers. It combines feedback, reviews, and ratings gathered from users. A positive experience can build a strong reputation, while negative experiences can damage it. This reputation model helps users and service providers assess which services are of good quality based on reviews from previous customers.

When customers have a good experience with a web microservice, they are more likely to recommend it to others. Conversely, if their experience is negative, they will likely leave critical feedback.

#What is Sentiment Analysis?

Sentiment analysis, or SA, is the process of evaluating the opinions, emotions, or attitudes of users concerning specific services or products. It allows organizations to classify sentiments in reviews, which can be divided into three main steps:

1. Sentiment Identification: Recognizing the general sentiment in a review.
2. Feature Selection: Identifying which aspects of the service are being discussed.
3. Sentiment Classification: Assigning a label to the sentiment, such as positive, negative, or neutral.

This analysis is typically performed at different levels, including document-level, sentence-level, and aspect-level. This article focuses on the sentence-level analysis, where opinions are grouped and evaluated to determine their sentiment.

#Related Works

In recent years, various methods for assessing the reputation of web services have been proposed. Some focus on collaborative solutions that involve user experience metrics to calculate trust scores. Others utilize machine learning techniques to predict which services are trustworthy. However, many of these methods come with limitations such as:

• Limited Scope: Often depend on feedback from a small group of users, which may not represent the larger customer base.
• Difficulty in Interpretability: Complex algorithms can make it hard to discern how reputation scores are calculated.
• Computational Requirements: Some methods need significant computer power, making them less suitable for devices with limited resources.

These limitations can lead to biased reputation scores, making it difficult to assess the quality of services accurately.

#Proposed Approach

This study proposes a new approach to evaluate the reputation of web services using deep learning models, particularly focusing on sentiment analysis. This approach consists of four phases:

1. Data Collection: Gather reviews from various platforms and clean the data by removing invalid entries and unnecessary characters.
2. Embedding Generation: Convert words in reviews into numerical representations that can be processed easily by a model.
3. Sentiment Analysis: Use deep learning techniques, specifically Long Short-Term Memory (LSTM) networks, to classify the sentiments of reviews.
4. Reputation Assessment: Calculate the reputation score based on the analysis of sentiments from user feedback.

#Data Collection Phase

Initially, the review dataset is examined to filter out invalid entries, such as those that are empty or mainly consist of spam. Next, the text is broken down into individual words, and irrelevant words are removed. Only significant words, like nouns and adjectives, are kept, as they provide crucial information for sentiment classification.

#Embedding Generation Phase

In this phase, words are converted into numerical forms. Each unique word gets a corresponding numerical value, and sentences are adjusted to a fixed length for uniformity. This helps in preparing the data for analysis.

#Sentiment Analysis Phase

In this section, we apply the LSTM model to classify the sentiments of reviews. LSTM is a type of neural network designed to handle complex patterns in sequential data, making it particularly effective for understanding the context in text data. It helps in retaining relevant information and discarding unnecessary details which improves the prediction performance.

#Reputation Assessment Phase

After sentiment classification, we assess the reputation of web microservices using a specific formula that takes into account the positive and negative sentiments gathered. This formula gives us a reputation score that reflects the overall quality as perceived by users based on their feedback.

#Implementation Environment

To execute this study, a powerful computer system was used. The reviews were collected from multiple review websites about various Amazon web microservices. The total dataset included over 10,000 reviews, most of which were positive. After filtering and cleaning, the data underwent a two-stage labeling process to ensure accuracy in sentiment classification.

#Performance Metrics

To evaluate how well the proposed model works, we measured its performance using several metrics, including overall accuracy, precision, recall, and F1-score. These metrics help assess how well the model is doing in classifying sentiments correctly.

#Results and Discussion

The main aim was to accurately classify the reviews. Different deep-learning models were tested, and the results showed that the proposed method performed better than traditional models. The implementation demonstrated impressive accuracy, proving that it can effectively assess the reputation of microservices based on user reviews.

#Conclusion and Future Work

This work showcases the potential of using deep-learning models to classify user reviews for web microservices. The LSTM model outperformed other conventional models in this analysis. The next steps will include integrating advanced language processing techniques to further improve sentiment analysis precision. We may also explore how to manage feedback that may be misleading and propose solutions to enhance the reputation assessment process.

By applying these approaches, we aim to provide a clearer understanding of web microservices' reputations based on user experiences, ultimately leading to better decision-making for customers and service providers alike.