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Sentiment Analysis

Customers express their opinions in complex ways for businesses. From analyzing user reviews to enhancing the businesses, Sentiment analysis plays a significant role.

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Sentiment Analysis

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  1. Sentiment Analysis

  2. Support Vector Machines Unsupervised Learning Linear Tree Classifiers Neural Network Machine Learning Approach Decision Tree Classifiers Supervised Learning Rule-Based Classifiers Naïve Bayes Probabilistic Classifiers Sentiment Analysis Bayesian Network Maximum Entropy Statistical Corpus-Based Approach Lexicon-Based Approach Semantic Dictionary-Based Approach Mitosis Technologies 2

  3. Sentiment identifies the positive or negative opinion within a sentence, paragraph or complete document. analysis is a text-based process that By applying natural language processing (NLP) and text analysis techniques we analyse unstructured data and extract significant information from a sentence. It is transformed into effective business intelligence. Sentiment Analysis This emotions to convert them into factual data. helps in analysing and measuring human The converted data allows us to categorise expressions as positive, negative or neutral. Mitosis Technologies 3

  4. Synonymous and Interchangeable Names Sentiment Analysis Appraisal Extraction Subjective Analysis Opinion Mining Review Mining Mitosis Technologies 4

  5. Sentiment analysis helps a business by identifying the attitudes, emotions and opinions of its customers about its products, services and brand. This is achieved by analysing social networking sites and other digital media forums where people are commenting on its products and services. Sentiment Analysis in Business Sentiment analysis identifies the most significant expressions and feelings of customers that could have the greatest impact on the business and its brand. Sentiment analysis helps a business by listening to its customers' emotions from survey responses, social media conversations and more. It can then customise its offerings to meet customers’ expectations in terms of pricing plans,ease of access, customerservice, etc. Mitosis Technologies 5

  6. Sentiment analysis uses rules-based, automatic and hybrid methods and algorithms. The rules-based approach helps identify subjectivity, polarity and the subject of an opinion. It employs techniques suchas: Stemming, tokenisation, part-of-speech tagging and parsing Process of Sentiment Analysis Lexicons (i.e. lists of words andexpressions) The automatic approaches use machine learning techniques. Hybrid approaches offer more power by combining elements of the rules-based and automatic approaches. Mitosis Technologies 6

  7. Sentiment Analysis CollectData Analysis Data Indexing Delivery Algorithms tag sentences based on polarity and intensity of sentiments Social Media,blogs posts, Twitter, news, product reviews Algorithms process the data and perform sentence splitting Provides the outcome of the sentiment analysis Mitosis Technologies 7

  8. The first step in the process is to collect customers’ public posts across the main social media platforms that reference the business’s products or services. These are then analysed using a feature extractor with the results fed into a machine learning (ML) algorithm. Process of Sentiment Analysis The ML text classifier transforms the extracted text into a “bag of words” and “n-grams” with their associated frequencies. The n-grams are then classified by a statistical model that produces customer insight and predictions. Mitosis Technologies 8

  9. Naïve Bayes - A probabilistic algorithm to predict text categories. Linear Regression - A statistical algorithm to predict the value from a set of features. Types of Algorithms Support Vector Machines - A non-probabilistic algorithm to categorise the text based on the similarities within it. Deep Learning - A diverse set of algorithms simulating a human brain by applying neural networks to process data. Mitosis Technologies 9

  10. Accurate classifiers involve identifying subjective and objective pieces of text and analysing their tone. Text without context is analysed by using pre-process or post-process techniques. Text Classification Sometimes a negative response can be expressed using positive words, as occurs with sarcasm. Algorithms such as MapReduce can be used to detect sarcasm. Commonly used emojis and Unicode characters can also be pre-processed to improve analysis results. We can define neutral text by classifying it into objective text, irrelevant information or text containing wishes. Mitosis Technologies 10

  11. Language- Independent Analysis I love the Boat headsets :D Pos Pos :) :D :-) =) I love the Boatheadsets The service could have been better -.- It was a bad tour :( Neg Theservicecould have been better Neg :( :/ :-( -.- Brie cheese is yum ^^ Sentiment indicators are assigned toemoticons Social media posts with emoticons are read by the algorithm Social media posts get labelled as positive or negative Mitosis Technologies 11

  12. Social Media Monitoring Brand Monitoring Sentiment Analysis Applications Voice of Customer (VoC) Customer Service Market Research Mitosis Technologies 12

  13. Scikit-learn Keras Common APIs Used in Sentiment Analysis PyTorch NLTK SpaCy OpenNLP TensorFlow CoreNLP Mitosis Technologies 13

  14. Text Processing It performs word grouping (“lemmatisation”), word stemming, parts of speech tagging and chunking, phrase extraction, date extraction, location and named entity recognition, and more. Tweet Sentiments Twitter is a commonly used platform for customers to express opinions on products. Tweet Sentiments analyse both new and existing tweets to extract the emotions one tweet at a time. Example SentimentAnalysis Software Types MLAnalyser This software uses machine learning to perform text classification, article summarisation, stock symbol extraction, and name, location and language detection. Mitosis Technologies 14

  15. Sentiment analysis is used to gain valuable insights from customers not easily achieved by other means. It is about enhancing a business and its brand in the eyes of its current and future customers. Sentiment Analysis in Brand Marketing Sentiment analysis reports are directly usable in showing key areas for improvement. In conclusion, sentiment analysis enables a business to gain new insights, understand empower its teams effectively for more productive work. its customers and Mitosis Technologies 15

  16. What can Sentiment Analysis do for Brands? Sentiment Analysis How do the company’s policies, external events or employees impact customers’ perception of its brand? What docustomers think of the products and brand? Are customers happy with the services they receive? What do customers like about the brand’s competitors? Mitosis Technologies 16

  17. What can Sentiment Analysis do for Brands? Increase Resolve CustomerExperience Pain Points Optimise Customer Service Customer Retention Measure Social Media Rol Optimise Pricing Mitosis Technologies 17

  18. To assist you with our services please reach us at: hello@mitosistech.com www.mitosistech.com IND: +91-7824035173 US: +1-(415)251-2064

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