_In the rapidly evolving world of Natural Language Processing (NLP), understanding human emotions behind the text is essential for businesses to stay ahead. Sentiment Analysis Model Training equips AI with the necessary tools to recognize and process consumers’ true feelings and opinions, leading to better customer engagement and improved online retail experiences._

## **The customer**
Our client is one of the largest online retailers in the world.

## **The context**
NLP technology is undoubtedly changing the way businesses interact with consumers. However, although AI models are becoming smarter at listening, understanding, and reacting, they still struggle with understanding sentiment – that is, the emotions, mood, or beliefs behind a person’s words.

Take this customer review of the (very expensive) Samsung’s UN85S9 HDTV, for example:
“My wife and I bought this after selling our daughter Amanda into white slavery. We actually got a refurbished. It’s missing the remote, but oh well — for $10K off I can afford a universal, right? The picture is amazing. I’ve never seen the world with such clarity. Amanda, if you’re reading this, hang in there. honey! We’ll see you in a year!”

> The sarcasm in this review is evident to any human reader, however with words like "amazing" and "clarity", it could be easy for an algorithm to mistake this as an authentically positive review.

Companies are working hard to create models that don’t make mistakes like these, building models trained to look beyond superficial understandings of what people are saying to examine the nuances of how it’s being told in short and long-form writing. That’s why they are investing in sentiment analysis (also known as opinion mining), a sub-field of Natural Language Processing (NLP) that tries to identify the opinion behind any piece of text.

Our client is one of these visionary companies. They aimed to create a sophisticated sentiment model to deliver broad-level composite scores (positive, negative, and neutral) for long-form content while simultaneously sifting through individual paragraphs, sentences, and words to extract granular insights.

## **The solution**
#### _Step 1 – Document segmentation_
To create the model, our client provided us with more than 100,000 documents, ranging from short paragraphs of reviews from their e-commerce site to full-length 1,500-word articles published online. We began by analyzing those documents and developing an optimal segmentation methodology for Sentiment Analysis Model Training. On average, we cut each document into 4 distinct pieces, though the variance was wide-ranging. The longest document had 84 unique segments.

#### _Step 2 – Sentiment annotation_
Our crowd tagged the sentiment of each individual segment while also providing high-level sentiment scores for each document as a whole. During that Sentiment Analysis Model Training process, we ran a wide range of automated gatekeeping procedures to monitor their quality of work in real-time. Ultimately, we sourced half a million annotations on the original 100,000 documents.

The high-quality data we provided ensured our client’s Sentiment Analysis Model Training could accurately detect the sentiment behind text 97.3% of the time.

![29c77897-4a68-4bf4-83ad-977128f729ac.png](https://prdstrapimediastorage.blob.core.windows.net/prdstrapimediastorage/assets/29c77897_4a68_4bf4_83ad_977128f729ac_ce14105bfd.png)

Ready to elevate your online retail experience with sentiment analysis? Check our available solutions on our [Marketplace](https://getdata.defined.ai/default/nlp/custom-data-for-nlp) or contact us today to get started on creating a tailored solution for your business!

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White Papers

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Toward Universally Ethical AI

Named Entity Recognition

Aspect-Based Sentiment Analysis

Case Studies

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Creating Various Test Corpora for ASR Bias Detection

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