How Amazon uses text mining to analyze customer reviews

Amazon Text Mining Principles and Structure

Sangsun Moon
How Amazon uses text mining to analyze customer reviews

When managing large volumes of documents, the ability to quickly find important information is essential. Companies are using text analytics to derive actionable insights from a variety of unstructured data sources. They also analyze feedback, such as customer surveys or social media comments, in their decision-making process.

In this article, we'll explain Amazon's text mining strategy and logic, as well as the key things you need to do to make it work for your organization.

How Amazon's text mining strategy works

Amazon is one of the most prominent companies using text mining. Amazon uses text mining for data collection and preparation, sentiment analysis, topic modeling, feature extraction, and opinion mining.

Text mining solutions help organizations find information quickly and accurately, enabling them to gain more useful insights and get products to market faster. They can also connect information across thousands of sources and text documents and ensure the right information is available. These advances have led to an increased ability to mitigate risk through text mining technology.

Compared to the traditional cookie-based approach, contextual advertising analyzes the text on a webpage to gain a deeper understanding of the content. Using text mining in your digital advertising strategy can yield significant results in terms of delivering targeted ads. Text mining can also help you manage large amounts of information more effectively, enriching your content and improving your metadata management processes.

How Amazon uses text mining to analyze customer reviews
Text mining solutions help organizations find information quickly and accurately, and they connect information across thousands of sources and text documents.

Beyond this, there are countless other ways in which text mining can be utilized internally within a company, but the one that has gained the most traction with retailers lately is "social media data analysis" along with the "customer service improvement" aspect. Text mining and natural language processing can help your customer care team tremendously. This is because text mining is at the core of chatbots that provide quick and automated responses to customers.

Across the enterprise, social media is also considered a valuable source of market and customer intelligence. With a textmining strategy, companies can also contextualize large amounts of social commentary to extract sentiment and sentiments that reveal positive and negative opinions about brands/products and consumers.

Text mining for product/service feedback analysis

Text mining is the process of reading and understanding human written text using computer systems for business insights. Text mining software can classify, sort, and extract information from text to identify different pieces of data. Examples include

  • Patterns
  • Relevance
  • Sentiment
  • Other actionable knowledge

Text mining allows you to accurately process multiple text-based sources such as emails, documents, social media content, and product reviews. Businesses use text mining tools to extract actionable insights from a variety of unstructured data sources. But the sheer volume of text from these sources would be overwhelming without software, whereas with software, the process is fully automated. Efficiency is increased, and accuracy goes beyond what humans can do.

Amazon text mining principles and structure

First, Amazon collects a large number of customer reviews for each product stored in its database. This raw text data is preprocessed to remove unnecessary data and noise, such as HTML tags, punctuation, and non-words. You may also use techniques such as tokenization or headline extraction such as morphological analysis.

Analyze the preprocessed text data to determine the sentiment of each review using techniques such as machine learning or rule-based algorithms. The sentiment analysis algorithm is able to determine whether a review is positive, negative, or neutral.

Once the sentiment of each review is determined, the text data is analyzed to identify themes. This is where topic modeling algorithms like Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF) come into play.

  • LDA is a technique for extracting hidden topics within given data. It can find topics composed of groups of words within documents that are represented by a mixture of topics. You don't need to know in advance what the topics will look like, you can simply tune the LDA parameters to fit different dataset shapes to form topics and explore the resulting document clusters.
  • NMF is a high dimensional data analysis tool that automatically extracts significant or sparse features from a set of data vectors. Given a set of documents, NMF can identify topics and categorize documents around different topics.

Topic modeling algorithms are used to extract features from textual data, such as words or phrases that are relevant to each topic and occur frequently. This allows Amazon to identify the main issues or concerns that customers have with each product.

The extracted features are then used to classify each review into one or more categories, such as product quality, customer service, or shipping time - i.e., labeling each review as a complaint about quality or a disappointment with shipping, among other comments collected. This is typically done using algorithms for supervised learning, such as Support Vector Machines (SVMs) or Naïve Bayes.

The results of text mining analysis can be visualized using graphs, charts, or word clouds. The resulting data can be used to quickly identify the most important issues or concerns and provide hints to track changes in customer sentiment over time.

Explore the feature extraction process, the heart of your text mining strategy

Text reviews can help you understand which features of your product customers are unhappy with. However, some products can have thousands of reviews, and it's difficult for a human to go through them all. This is where a system that provides statistical reports on how many reviewers were dissatisfied with a particular feature of a product comes in. This allows users to view reviews for any Amazon product category, or for a specific product, to look at customer reactions to each key feature.

Text preprocessing and vectorization

Preprocessing raw text data typically involves converting it into a numerical format that can be used by machine learning algorithms. This is usually done using vectorization techniques such as Bag-of-Words (BoW) or Term Frequency-Inverse Document Frequency (TF-IDF).

BoW creates a vector for each document. Here, each dimension corresponds to a unique word in the corpus and the value represents the frequency of the word in the document. TF-IDF is similar to BoW, but it also considers the importance of a word in the corpus based on its frequency across all documents.

In some cases, vectorized text data can be high-dimensional, with many dimensions corresponding to rare or uninformative words. To reduce the dimensionality of the data, you can apply techniques such as principal component analysis (PCA) or singular value decomposition (SVD). These techniques aim to identify the most important dimensions that explain the variance in your data.

Text mining is the art of seeing the big picture of customer reviews and gleaning insights from them.
Text mining is the art of seeing the big picture of customer reviews and gleaning insights from them.

Feature selection and encoding/scaling

After dimension reduction, you can apply feature selection techniques to identify the most useful features for a particular task. This involves selecting a subset of dimensions that are most closely related to a specific target variable or outcome.

Once the relevant features are identified, they are typically encoded using a format that can be used by machine learning algorithms. This process sometimes involves converting categorical variables to numerical variables using techniques such as one-hot encoding or label encoding.

Finally, you can scale your features so that they are on the same scale and have similar ranges. This can improve the performance of your machine learning algorithm and prevent certain features from becoming dominant over others. Common feature scaling techniques include normalization or min-max scaling.

Text Analysis with Amazon OpenSearch Service and Amazon Comprehend

"Text Analysis with Amazon OpenSearch Service and Amazon Comprehend" is an end-to-end solution for extracting meaningful insights from unstructured data. The solution uses Amazon Comprehend, a natural language processing (NLP) service, to perform text analysis and Amazon OpenSearch Service to index and analyze unstructured text. This provides efficient and affordable text analytics.

Businesses can use artificial intelligence and algorithms to analyze online reviews and gain insights into consumer preferences and brand image.

The future of your Amazon text mining strategy

By exploring online reviews, companies have the opportunity to expand their knowledge about aspects such as consumer preferences, brand image, and brand positioning. Historically, the qualitative nature of online reviews has made it difficult to analyze them on a comprehensive level and gain meaningful insights, but advances in artificial intelligence and algorithms have made it possible to generate and use electronic word-of-mouth (eWOM).

Various techniques of text mining can be used to explore online reviews. The machine learning algorithms described above used to be out of reach for small and medium-sized businesses because they required specialized computational skills, with "lack of skilled labor" and "feeling overwhelmed by the role" being among the main reasons why companies didn't adopt AI.

Lexicon-based methods, on the other hand, are said to be better suited to SMBs as they provide a simpler and more intuitive means of analyzing the text of online reviews, but have been criticized for their inability to capture emotions hidden in context.

For a text mining strategy to work, the AI needs to be able to read the hidden minds of consumers based on what it learns. Machine learning algorithms need accurate training data to learn better, and DataHunt has been building datasets with up to 99% accuracy by hiring skilled domestic labelers.

Conclusion: Amazon's approach to text mining is the process of using algorithms to analyze, label, and visualize sentiment.

  • Amazon analyzes text data to identify topics. This is done using topic modeling algorithms such as LDA and LMF.
  • The characteristics extracted by the algorithms are based on text preprocessing, and solutions are used to extract meaningful insights from unstructured data.
  • Online commerce companies like Amazon can use text mining strategies to read consumer sentiment and actively leverage eWOM for their business.

For businesses that need to stay on top of consumer needs in the future, a text mining strategy is definitely here to stay. Text mining is all about collecting and preprocessing real-world data, learning from it and extracting results, and finding "features" in topics. The processing of data has become important to make the process of analyzing massive amounts of data better.

At DataHunt, we work with around 500 skilled labelers to process highly accurate datasets. We have proven results for small, medium, and large enterprises. With an expert data processing partner, catching up with Amazon is not out of the question as it has captured the hearts of consumers around the world.

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