Human beings are the most advanced species on earth, thanks to our ability to communicate and share information. The concept of developing a language has contributed significantly to our success. Today, the human language is one of the most diverse and complex parts of us, considering a total of six thousand and five hundred languages that exist. However, with the rise of the internet and social media, data is being generated at an exponential rate, with only 21% of available data in structured form, leading to the need for text mining and natural language processing (NLP).
What is Text Mining and NLP?
Text mining, or text analytics, is the process of deriving meaningful information from natural language text. It usually involves structuring the input text, deriving patterns within the structured data, and finally evaluating and interpreting the output. On the other hand, NLP refers to the artificial intelligence method of communicating with an intelligent system using natural language. Text mining and NLP go hand-in-hand, with the overall goal being to turn text into data analysis.
Applications of Text Mining and NLP
- Sentiment AnalysisL Sentiment analysis is one of the most important applications of NLP, involving the identification and extraction of subjective information from text sources. Companies use it to understand how their customers feel about their products or services by analyzing their feedback on social media platforms such as Twitter and Facebook.
- Chatbots: Companies are increasingly using chatbots to interact with their customers. The process behind this is due to NLP. Chatbots use NLP to understand natural language text input, allowing them to communicate effectively with customers.
- Speech Recognition: Voice assistants such as Siri, Google Assistant, and Cortana rely on NLP to understand and respond to voice commands.
- Machine Translation: Machine translation is another use case of NLP. Google Translate uses NLP to translate data from one language to another in real-time.
- Advertisement: Matching NLP is used to match advertisements based on a user’s search history, enabling companies to target potential customers effectively.
Components of NLP
NLP is divided into two major components: natural language understanding and natural language generation. Natural language understanding refers to mapping the given input into natural language into useful representation and analyzing those aspects of the language, whereas generation is the process of producing meaningful phrases and sentences in the form of natural language from some internal representation.
Steps Involved in NLP
- Tokenization: Tokenization is the process of splitting strings into tokens, which are small structures or units that can be used for tokenization. It is useful in the NLP process.
- Stemming: Stemming refers to normalizing words into their base or root form. The stemming algorithm works by cutting off the end or the beginning of the word, taking into account a list of common prefixes and suffixes that can be found in an infected word.
- Lemmatization: Lemmatization takes into consideration the morphological analysis of the word. It groups together different infected forms of the word called lemma and maps them into one common root. The output of lemmatization is a proper word.
- POS Tags: The grammatical type of a word is referred to as POS tags or the parts of speech. It indicates how a word functions in meaning as well as grammatically within the sentence. A word can have more than one part of speech based on the context in which it is used.
Text mining and NLP are becoming increasingly important due to the vast amount of data being generated every day. Companies use NLP and text mining techniques to analyze data and derive meaningful insights. The different applications of NLP and the various components of the NLP process are essential to understand how NLP and text mining work.