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text stemming in NLP

Human language is an unsolved problem that there are more than 6500 languages worldwide. The tons of data are generating every day as we speak, we text, we tweet, from voice to text on every social application and o get the insights of these text data we need technology as NLP. If you know there are two types of data are there one is structured and unstructured data. Structured data used for Machine learning models and unstructured data is used for Natural language processing. There are only 21% of structured data is available, so now you can estimate how much NLP is required to handle unstructured data. 

To get the insights of the dataset of unstructured data to take out the important information from it. The important technique to analyze the text data is text mining. Text mining is the technique to extract useful information from the unstructured data by identifying and exploring a large amount of text data. Or we can say that text mining is used to convert the unstructured data to the structured dataset.

Normalization, lemmatization, stemming, tokenization is the technique in NLP to get out the insights from the data.

Now we will see how text stemming works?

Stemming is the process of reducing inflection in words to their “root” forms such as mapping a group of words to the same stem. Stem words mean the suffix and prefix that have added to the root word. It is the process to produce grammatically variants of root words.  A stemming is provided by the NLP algorithms that are stemming algorithms or stemmers. The stemming algorithm removes the stem from the word. For example, eats, eating, eatery, they are made from the root word “eat“. so here the stemmer removes s, ing, very from the above words to take out meaning that the sentence is about eating something. The words are nothing but different tenses forms of verbs.

This is the general idea to reduce the different forms of the word to their root word.
Words that are derived from one another can be mapped to a base word or symbol, especially if they have the same meaning.

As we can not sure that it will give us a 100% result so we have two types of error in stemming they are: over stemming and under stemming.

Over stemming occurs when there are too many words have cut out.
This could be known as non-sensical items, where the meaning of the word has lost, or it can not be able to distinguish between two stems or resolve the same stem where they should differ from each other.

For example, take out the four words university, universities, universal, and universe. A stemmer that resolves these four stems to “Univers” that is over stemming. It should be the universe stemmer that stemmed together and university, universities stemmed together they all four are not fit for the single stem.

Under stemming: Under-stemming is the opposite of stemming. It comes from when we have different words that actually are forms of one another. It would be nice for them to all resolve to the same stem, but unfortunately, they do not.

This can be seen if we have a stemming algorithm that stems from the words data and datum to “dat” and “datu.” And you might be thinking, well, just resolve these both to “dat.” However, then what do we do with the date? And is there a good general rule? So there under stemming occurs.

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Necessity of Machine Learning in Retail

Nowadays data proves to be a powerful pushing force of the industry. Big companies representing diverse trade spheres seek to make use of the beneficial value of the data. Thus, data has become of great importance for those willing to take profitable decisions concerning business. Moreover, a thorough analysis of a vast amount of data allows influencing or rather manipulating the customers decisions. Numerous flows of information, along with channels of communication, are used for this purpose.

The sphere of the retail develops rapidly. The retailers manage to analyze data and develop a peculiar psychological portrait of a customer to learn his or her sore points. Thereby, a customer tends to be easily influenced by the tricks developed by the retailers.

This article presents top 10 data science use cases in the retail, created for you to be aware of the present trends and tendencies.

  1. Recommendation engines

    Recommendation engines proved to be of great use for the retailers as the tools for customers’ behavior prediction. The retailers tend to use recommendation engines as one of the main leverages on the customers’ opinion. Providing recommendations enables the retailers to increase sales and to dictate trends.Recommendation engines manage to adjust depending on the choices made by the customers. Recommendation engines make a great deal of data filtering to get the insights. Usually, recommendation engines use either collaborative or content-based filtering. In this regard, the customer’s past behavior or the series of the product characteristics are under consideration. Besides, various types of data such as demographic data, usefulness, preferences, needs, previous shopping experience, etc. go via the past data learning algorithm.Then the collaborative and content filtering association links are built. The recommendation engines compute a similarity index in the customers’ preferences and offer the goods or services accordingly. The up-sell and cross-sell recommendations depend on the detailed analysis of an online customer’s profile.

  2. Market basket analysis

    Market basket analysis may be regarded as a traditional tool of data analysis in the retail. The retailers have been making a profit out of it for years. This process mainly depends on the organization of a considerable amount of data collected via customers’ transactions. Future decisions and choices may be predicted on a large scale by this tool. Knowledge of the present items in the basket along with all likes, dislikes, and previews is beneficial for a retailer in the spheres of layout organization, prices making and content placement. The analysis is usually conducted via rule mining algorithm. Beforehand the data undertakes transformation from data frame format to simple transactions. A specially tailored function accepts the data, splits it according to some differentiating factors and deletes useless. This data is input. On its basis, the association links between the products are built. It becomes possible due to the association rule application.The insight information largely contributes to the improvement of the development strategies and marketing techniques of the retailers. Also, the efficiency of the selling efforts reaches its peak.

  3. Warranty analytics
    Warranty analytics entered the sphere of the retail as a tool of warranty claims monitoring, detection of fraudulent activity, reducing costs and increasing quality. This process involves data and text mining for further identification of claims patterns and problem areas. The data is transformed into actionable real-time plans, insight, and recommendations via segmentation analysis.The methods of detecting are quite complicated, as far as they deal with vague and intensive data flows. They concentrate on the detecting anomalies in the warranty claims. Powerful internet data platforms speed up the analysis process of a significant amount of warranty claims. This is an excellent chance for the retailers to turn warranty challenges into actionable intelligence.
  4. Price optimization
    Having a right price both for the customer and the retailer is a significant advantage brought by the optimization mechanisms. The price formation process depends not only on the costs to produce an item but on the wallet of a typical customer and the competitors’ offers. The tools for data analysis bring this issue to a new level of its approaching. Price optimization tools include numerous online tricks as well as secret customers approach. The data gained from the multichannel sources define the flexibility of prices, taking into consideration the location, an individual buying attitude of a customer, seasoning and the competitors’ pricing. The computation of the extremes in values along with frequency tables are the appropriate instruments to make the variable evaluation and perfect distributions for the predictors and the profit response.The algorithm presupposes customers segmentation to define the response to changes in prices. Thus, the costs that meet corporate goals may be determined. Using the model of a real-time optimization the retailers have an opportunity to attract the customers, to retain the attention and to realize personal pricing schemes.
  5. Inventory management
    Inventory, as it is, concerns stocking goods for their future use. Inventory management, in its turn, refers to stocking goods in order to use them in time of crisis. The retailers aim to provide a proper product at a right time, in a proper condition, at a proper place. In this regard, the stock and the supply chains are deeply analyzed. Powerful machine learning algorithms and data analysis platforms detect patterns, correlations among the elements and supply chains. Via constantly adjusting and developing parameters and values the algorithm defines the optimal stock and inventory strategies. The analysts spot the patterns of high demand and develop strategies for emerging sales trends, optimize delivery and manage the stock implementing the data received.
  6. Location of new stores
    Data science proves to be extremely efficient about the issue of the new store’s location. Usually, to make such a decision a great deal of data analysis is to be done. The algorithm is simple, though very efficient. The analysts explore the online customers’ data, paying great attention to the demographic factor. The coincidences in ZIP code and location give a basis for understanding the potential of the market. Also, special settings concerning the location of other shops are taken into account. As well as that, the retailer’s network analysis is performed. The algorithms find the solution by connection all these points. The retailer easily adds this data to its platform to enrich the analysis opportunities for another sphere of its activity.
  7. Customer sentiment analysis
    Customer sentiment analysis is not a brand-new tool in this industry. However, since the active implementation of data science, it has become less expensive and time-consuming. Nowadays, the use of focus groups and customers polls is no longer needed. Machine learning algorithms provide the basis for sentiment analysis.The analysts can perform the brand-customer sentiment analysis by data received from social networks and online services feedback. Social media sources are readily available. That is why it is much easier to implement analytics on social platforms. Sentiment analytics uses language processing to track words bearing a positive or negative attitude of a customer. These feedback become a background for services improvement.

    The analysts perform sentiment analysis on the basis of natural language processing, text analysis to extract defining positive, neutral or negative sentiments. The algorithms go through all the meaningful layers of speech. All the spotted sentiments belong to certain categories or buckets and degrees. The output is the sentiment rating in one of the categories mentioned above and the overall sentiment of the text.

  8. Merchandising
    Merchandising has become an essential part of the retail business. This notion covers a vast majority of activities and strategies aimed at increase of sales and promotion of the product. The implementation of the merchandising tricks helps to influence the customer’s decision-making process via visual channels. Rotating merchandise helps to keep the assortment always fresh and renewed. Attractive packaging and branding retain customers attention and enhance visual appeal. A great deal of data science analysis remains behind the scenes in this case.The merchandising mechanisms go through the data picking up the insights and forming the priority sets for the customers, taking into account seasonality, relevancy and trends.
  9. Lifetime value prediction
    In retail, customer lifetime value (CLV) is a total value of the customer’s profit to the company over the entire customer-business relationship. Particular attention is paid to the revenues, as far as they are not so predictable as costs. By the direct purchases two significant customer methodologies of lifetime predictions are made: historical and predictive.All the forecasts are made on the past data leading up to the most recent transactions. Thus the algorithms of a customer’s lifespan within one brand are defined and analyzed. Usually, the CLV models collect, classify and clean the data concerning customers’ preferences, expenses, recent purchases and behavior to structure them into the input. After processing this data we receive a linear presentation of the possible value of the existing and possible customers. The algorithm also spots the inter dependencies between the customer’s characteristics and their choices. The application of the statistical methodology helps to identify the customer’s buying pattern up until he or she stops making purchases. Data science and machine learning assure the retailer’s understanding of his customer, the improvement in services and definition of priorities.
  10. Fraud detection
    The detection of fraud and fraud rings is a challenging activity of a reliable retailer. The main reason for fraud detection is a great financial loss caused. And this is only a tip of an iceberg. The conducted profound National Retail Security Survey goes deeply into details. The customer might suffer from fraud in returns and delivery, the abuse of rights, the credit risk and many other fraud cases that do nothing but ruin the retailer’s reputation. Once being a victim of such situations may destroy a precious trust of the customer forever.The only efficient way to protect your company’s reputation is to be one step ahead of the fraudsters. Big data platforms provide continuous monitoring of the activity and ensure the detection of the fraudulent activity. The algorithm developed for fraud detection should not only recognize fraud and flag it to be banned but to predict future fraudulent activities. That is why deep neural networks prove to be so efficient. The platforms apply the common dimensionality reduction techniques to identify hidden patterns, to label activities and to cluster fraudulent transactions. Using the data analysis mechanisms within fraud detection schemes brings benefits and somewhat improves the retailer’s ability to protect the customer and the company as it is.
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