Using AI To Analyze Text With Natural Language Processing

Using Machine Learning Models to Analyze and Leverage Text Data

Every business uses text to promote, improve and update its services and products. Natural Language Processing (NLP) is a subfield of computer science and artificial intelligence is focused on the method of extracting meanings and data from texts through machines learning techniques.

Through the use of machine learning algorithms and methods that can help organizations solve the most common problems with text data like identifying various categories of users, determining the purpose of a message, and precisely identifying various categories of feedback and user reviews. When text data is analyzed by using deep learning models the right responses are produced.

Use the techniques below to analyze text data and to solve every problem with text to improve your product or service.

1. Organize your data

IT departments are faced with a huge amount of data every  day. The first step to leverage the text and solving issues that are related to text is to arrange or collect the information based on its importance.

For example, let's create an example of a data set that includes the word "Fight." When organizing data sets like tweets or posts on social media that contain this particular keyword, we'll have to categorize them according to the context importance. The ultimate goal is to report instances that involve physical violence to the local authorities.

Thus, data must be distinguished based on the context in which it is used. Does the word in its context suggest a sport organized such as a boxing contest or does its meaning refer to an argument or disagreement that is not a physical harm? The word could also mean physical fight or a brawl and is what we are looking for in our text. It may also refer to a struggle to end a social issue such as, "a fight for justice."

This leads to the need for labels to distinguish between words that have relevance (that could suggest physical combat or brawl) and those that are not relevant (every other meaning for the word). Labeling data and then training a deep-learning model results in quicker and more efficient results when solving textual problems.

2. Cleanse your data

After you have gathered your data, it has to be cleaned in order to provide efficient and seamless model training. The reason for this is quite simple: clean data is much easier to analyze and process using a deep learning model. Here a

There are several ways to cleanse your files;

Remove non-alphanumeric characters like symbols (currency symbols and punctuations) could contain significant information, they could cause data to be difficult to analyze for various types of models. One way to tackle this issue is by getting rid of them or limiting their usage to those that require text like the use of hyphens within the term "full-time."

Utilizing tokenization: Tokenization entails breaking strings into multiple pieces known as tokens. The tokens chosen could include phrases (sentence tokenization) or words (word tokenization). When using sentence tokenization (also called sentence segmentation) the text string is divided into smaller sentences Word tokenization breaks down text into its constituent words.

Use Lemmatization: Lemmatization can be an effective method to clean information using morphological and vocabulary word analytics to reduce the related words back to their standard grammar form, also called Lemma. For instance, Lemmatizations can remove inflections in order to return a word back to its dictionary form.

3. Use Accurate Data Representation

Algorithms cannot process data in text therefore data must be represented by a set of numbers which algorithms can process. This is known as vectorization.

The most natural method to do this is to encode every character as numbers so that the classifier is able to learn the structure of every word in the data but this isn't realistically feasible. Thus, a better way of representing data in our systems or in the classifier is to assign the number of a particular word. Therefore, every sentence is depicted as a listing of numbers.

The representational models referred to as the Bag of Words (BOW) it is only the frequency of the words are considered , and not the sequence or order of words within the text. All you have to do is to

Choose a suitable method of designing your vocabulary for tokens (known words) that are used and to evaluate their inclusion within the texts.

BOW is a method of determining the meaning. BOW technique is founded on the idea that the more often words appear in text greater the degree to which it conveys its significance.

Classify Your Text Data collection to Improve AI Interpretation

4. Sort your data

Texts that are unstructured are everywhere; they can be found present in chats, emails messages, responses to surveys and many other forms of. The process of extracting useful information from text that is not structured can be difficult and one method to overcome this is to use text classification.

Text classification (also known as text categorization, or text tag) cleanses a text using categories or tags to identify the elements of a text in accordance with the content. For instance product reviews can be classified based on intent or intent, articles can be classified by topical relevance as well as conversations within a chatbot can be classified according to the urgency. Text classification can aid in the detection of spam and sentiment analysis of data.

The classification of text can be achieved by hand or automated. Manual text classification is when humans make annotations on texts, then interprets it , and classifies it according to. Naturally, this process is lengthy and time-consuming. The method that is automated uses algorithms and models of machine learning to categorize the text based on certain guidelines.

With the BOW model Text classification analytics discern patterns and emotions in the text by analyzing its frequency group of words.

5. Check your data

Once you have completed the process of interpreting and processing your data using machine-learning models, it's important to examine them for mistakes. One effective method of visualizing the data for inspection is to use an ambiguity matrix. The name is used to determine if there has two labels in confusion. In this case, it is the class that is relevant and not important.

An confusion matrix also known as the error matrix helps you to see the output performance that an algorithm produces. The information is presented in a table layout where each row in the matrix represents a part in a predicted label , and each column represents a specific component of the real label.

In our case we trained the classification to make a distinction between physical as well as non-physical ones (such as the Civil rights movements that are non-violent). If the sample consisted of 22 events, twelve physical and 10 non-physical the confusion matrix will be used to represent the results in an arrangement of tables as follows:

In this confusion matrix out of the 12 actual physical fights the algorithm predicted that there were seven nonviolent protests or fights. In addition, the system predicted that, of the 10 actual protests it was three actual physical battles. The right predictions are highlighted. They are those that are true (TP) and the true negatives (TN) respectively. Other results include False negatives (FN) and false positives (FP).

Therefore, when understanding and validating the results of our predictions made using the model we should make use of the appropriate words that are used as classifiers. The most appropriate words to categorize fights that are not physical in a text include marches, protests peaceful, non-violent and demonstrations.

If the data is properly analyzed and analyzed, information, systems will then efficiently respond.

Leveraging Text Data to Generate Responses: A Case for Chatbots

Following the cleaning, analyzing, and decoding text The next step is to provide the appropriate response. This is the method utilized by chatbots.

Chatbot response models generally come in two varieties namely retrieval-based models as well as generative models. Retrieval-based models rely on the predetermined set of responses that are automatically pulled depending on input. This makes use of a an algorithm to determine the most suitable response. In contrast the generative models don't employ predefined responses, instead they generate new responses by machine translators.

Each method has pros and cons , and both have suitable applications. In the first place, because they are pre-defined and written, retrieval-based methods don't make grammatical mistakes However, when there is no previously registered output for an unknown input (such as the name) These methods might not give the desired results. It needs various screenshots or pictures through Image Data Collection process of chats for training purpose.

Generative techniques are more advanced in their use and "smarter" as responses are generated in real-time and are dependent on what the user is attempting to input. However, because they require intensive training, and responses are not written out, they can be grammatically incorrect.

In both approaches to response-generation, length of conversation may be a challenge. If the length of the input, or the conversation is, the more difficult to automate responses. For open domains conversations are free and inputs can be taken at any time. Thus, open domains can't be constructed using the basis of a retrieval-based chatbot. In closed domains, in which there is a limitation in terms of inputs and outputs (you have to answer only a number of questions) retrieval-based bots perform best.

Generative-based chat platforms can manage closed domains but require a sophisticated computer to manage conversations that last longer in open domains.

The issues that arise from lengthy or open-ended discussions include the following:

The incorporation of a physical and linguistic context: In lengthy conversations, people will keep the record of what was said , and it can be hard for the computer system to comprehend if this data is repeated throughout the course of conversation. Therefore, it is necessary to the inclusion of contexts for each word spoken, which isn't easy.

Maintaining Semantic Coherence several systems are programmed to give responses to a certain inquiry or input however, they might not be able to provide an identical or consistent response when the input is modified. For instance, you might want the same response to "what do you do?" and "what's your occupation?". It might be difficult to train systems that generate perform this.

Determining intent: To make sure that the relevance of a response to the input user's context, the program must to know the intention of the user. However, this isn't an easy task. This is why numerous systems generate generic responses when it's not required. For instance, "that's great!" because it is a generic response could not be appropriate for an input like "I live alone, outside the yard".

This is why retrieval-based techniques are still the easiest to employ for chatbots or other chat-based platforms.

How GTS can help you?

Global Technology Solutions understands the need of having high-quality, precise datasets to train, test, and validate your models. As a result, we deliver 100% accurate and quality tested datasets. Image datasets, Speech datasets, Text datasets, ADAS annotation and Video datasets are among the datasets we offer. We offer services in over 200 languages.