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In this world or present era millions of people are doing work online and uses different Ecommerce platforms and build up their business through it. But in order to maintain the data record or customers feedbacks there are certain methods of machine learning which plays an important role for the prediction of reviews and make a proper record. The main purpose of this article is to highlight the methods to detect the customers reviews by using machine learning.
I would like to describe following methods in detail which play an important role for the detection of customer reviews.
NLP (Natural Language Processing)
It is a branch of artificial intelligence. The main purpose of NLP is able to understand the text or words which are in the form of messages, voice as same as humans understanding of words. The text may be in the form of sentiments, reactions and actions of humans. Artificial intelligence set the priority that computer must able to perform and read any task which are in the form of reactions, actions and sentiments. It relates human language with computer learning machine, statistical analysis and other modeling process. So, under the supervision of these computer must be able to translate one language in to another language or it must be able to generate any information in the form of spoken, text in any language. These can be done with the help of sentiment words described in the sentence, speech or audio. Moreover, NLP is also helpful for streamline businessman who are promoting their work with the help of product link related to movies, sports and other streaming network. NLP analyzed the reviews in the form of audio, text so everyone can understand this in a better way. NLP set computer machine language to read and understand the meaning of sentence same as humans are doing on daily basis. NLP actually process and feed data from the beginning so machine language able to generate and understand all the speech. Different tasks of NLP used to analyze the human text and break down text and audio in meaningful words so it can be readable and understand. Following are the tasks of NLP includes:
It is known as the conversion of any speech in the form of text. The main purpose of this is to convert any speech which are not understandable and in which humans spoke fast and in different accent so speech recognition able to convert any human speech in the form of text so any one can read and understand those sentences which were spoken in different languages and were unable to read or listen correctly.
Word Sense disambiguation:
In word sense disambiguation sematic analysis used to focus on those words in a sentence which are similar but consider those which gives accurate meaning on the base of sentence.
Parts of Speech Tagging:
It understands the grammatical way of sentence with the help of verb, noun, adjectives describe in a sentence. Either a sentence is correct or consist of few grammatical mistakes in it.
Co reference Resolution:
It is basically used to focus on those repeating words or idioms used in the sentences. With the help of pronouns described in a sentence it can be judged easily the talk is related to person or object.
Named Entity Recognition:
It is useful to understand the sentence with the help of common places names or person names.
Natural Language Generation:
It is basically focused on human language words and it understand the collection of words appeared in a sentence.
It is basically used to find out sentiment words described in sentence like emotions, feelings, actions and reactions according to the mood of humans in different situations. With the help of those sentiment words, it is best way to understand any sentence meaning in less time.
Different tools are used to perform the NLP.
NLTK (natural language tool kit) and python libraries used to perform NLP technique on different data set. Moreover, it can also useful for tokenization, lemmatization, and for implementing semantic reasoning. Deep learning techniques which are based on recurrent neural network and convolutional neural network which provides options to NLP for better understand the text, audio and unstructured data. It is for the better learning of the data.
NLP is used in many applications for the better understanding of data set. Few are the most common applications in which NLP played an important role.
Social Media Sentiment Analysis:
In machine learning NLP tool used to understand the hidden data which are available on social media platforms. Meanwhile sentiment analysis first analyzes the language of people in which they were spoke different sentences on social media platforms or submit reviews and then extract information in the form of text.
NLP also played a very important role for the detection of fake emails, fake messages, fake reviews and work on those hidden data which includes any spam or torture material which is difficult to detect. It detects and scan all the data and then focus on spam things and give results on the basis of spam words available in the emails, messages or any other platform.
Virtual Agents and chatbots:
Speech recognition is used in apple and amazon platform to visualize the data in the form of patterns. In Apple with the help of speech detector patterns or locks in phone can easily be opened and patterns can also be opened by the detection of human voice. Moreover, chatbots options on many platforms we can get any help from different site chatbots online any time. We can type any question on those helpful chatbots and with the help of speech recognition chatbots understand the sentence meaning and display information according to the asking question. Another factor is that in apple phone NLP also plays an important role during face recognition when human face match then mobile lock open. NLP is also helpful in tesla car NLP detect voice and when voice of person match car starts and move functionally.
NLP techniques used to store large amount of data and then generates useful meaning of each sentence. Text summarization in NLP store data and work on it and display useful information so everyone can understand meaning of each sentence. Moreover, those people who don’t have enough time to read all the sentences text summarization generates only useful words so it is convenient for those people.
NLP in my data set is used to remove the stop words and generates those useful words which describes the meaning of whole sentence in less time. Remove repeatable words. Remove helping verb, conjunctions and irrelevant words. NLP read the reviews submitted by different customers and detect the words with the help of sentiment analysis.
Vectorization in machine Learning:
It is a technique used to execute the code more accurately and fast. The important point of this technique is to read any algorithm data when implement from scratch and gives accurate results by using numerical linear algebra libraries in python, R language, C++ .. etc. By using vectorization technique we can find out the solution of any problem or dataset and it analyze the code accurately in less time. Moreover, the rate of error appearing during execution is low. This technique is reduced the error rate as well. The thing is how the error rate will reduce? For this optimization algorithm in machine learning is used to overcome the error and tries to give the accurate results. Another important point of vectorization is to covert the text into numerical data. Several number of ways available to perform vectorization. Here are the following vectorization techniques:
1)Bag of words:
It considers as simple technique to perform tasks.
It further categorized into three operations like tokenization, vector creation and vocabulary creation.
Tokenization is used to read the text and consider only unique words described in the sentence to give particular meaning.
It is a step to form a sparse matrix for the input of data. Each row is a sentence vector and the length of matrix is equal to the size of the vocabulary in sparse matrix.
Vocabulary creation is a step used to consider only unique words selected from tokenized words. Then arrange words according to the alphabetic order.
2) Term Frequency-Inverse Document Frequency:
It is a numerical way to judge how important the number of words described in a particular text. The point is that is TF- IDF use for the improvement of bag of words?
In Bag of words vectorization worked on the frequency of the vocabulary of the words described in the text. But TF-IDF is used to remove the repeated words in a sentence or remove preposition, and consider only meaningful words which described the scenario of the sentence in a short way. Now look up how to find out the Term frequency and Inverse document frequency. Term frequency is actually focus on the frequency of the words described in text.
TF = Frequency of word in a Document/Total number of words described in that document
IDF used to focus on those words which describe the documents overview.
IDF = log (Total number of documents/Documents containing word W)
Important fact of IDF is to uses in chatbots to describe which word is important in the document it works and pick out the only those words which are important.
After the frequency-based methods, another method was discovered for the better understanding of the words and useful in NLP.
This is used for the word embeddings in neural network. Moreover, Word2Vec improves the TF and IDF as well. It picked up and focus on only those words which have same meaning but clearly describe the sentence. In Bag of words and TF-IDF each word treated separately but in case of Word2Vec every word represented as n-dimensional vector and words having similar meanings considered only suitable word which best described the document.
Global vectors (Glove) are used for the representation of words. As compared to Word2Vec love focus on both global and statistics for the better understand of words embedding. Glove works and built an idea. On the basis of idea word-word occurrence played a very important role in sentence and in statistics helpful for words embedding.
Fast text is more advanced way to focus on those unknown words which were unable to predict and read by using other ways. Because of its compatibility to read all words it performs well to read the text and capture words. In order to understand the better results, it focusses on letters instead of words and letters look like blocks.
LDA and Topic modelling:
Topic model is used to categorize the documents so it can be easily examined by users. Moreover, in the era of online platforms Topic model plays a significant role like in the case of cell phone reviews it focusses on the reviews submitted by different customers and then categorized the reviews in positive, negative and neutral reviews so we can easily analyze the reviews due to their categories. Topic modelling is unsupervised learning algorithm. Topic modelling is further categorized into the following techniques. Like Latent Semantic Analysis, Latent Dirichlet Allocation (LDA). LDA is used for topic modelling. It focusses on the background of the words which are hidden. The most important part of this technique is to study about the topics and describe each topic is mixture of words. LDA in python used the extract the topics on the basis of reviews submitted by different customers. It describes that each topic consists of top words which submitted by customers.
XGBOOST is a library used for the implement of gradient boost decision tree. In python XGBOOST fits with training data set and describes models as regressors or classifiers. It also considered for getting best results quickly. Due to its fast and accurate performance this library is used to solve many difficult tasks in machine learning.
Sentiment Analysis Using Vader:
In Machine learning Sentiment analysis is a technique tells us about the sentence or comment is positive, negative or neutral. In all platforms sentiment analysis used to analyze the structure of sentence and with the help of words like emotions, reactions and actions of humans it describes the main theme of sentence. For the understanding of online reviews submitted by customers Vader Sentiment analysis is used. Vader used to check the behavior of positive, negative and neutral reviews also calculate the score of positive, negative and neutral reviews of different customers submitted on social media. on the basis of qualitative and quantitative reviews the performance of Vader is typically same as human beings. It betters understand the dictionary words and understand the behavior of humans with the help of sentiment words described in sentence, speech, orders, reactions and actions. Vader improves the sentiment lexicons for the better understanding of sentence and perform fast to generate accurate results.