Text Analysis Techniques: NLP Explained




A digital brain processing various languages

Natural Language Processing (NLP) is a fascinating field of artificial intelligence that focuses on the interaction between computers and humans through natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of human language in a valuable way. This article will delve into the various text analysis techniques used in NLP, providing a comprehensive glossary of terms and concepts.

As we navigate through the digital age, the amount of data being generated is growing at an exponential rate. Much of this data is unstructured text, which can be a goldmine of information if analyzed properly. NLP techniques provide the means to extract insights and understand patterns from this unstructured text data. Let’s dive into the world of NLP and explore its various text analysis techniques.


Tokenization is the process of breaking down text into words, phrases, symbols, or other meaningful elements called tokens. The goal of tokenization is to understand the context of the text by analyzing its tokens. It is one of the most basic and crucial steps in NLP and forms the foundation for more complex techniques.

Tokenization can be performed in different ways, each with its own set of rules and considerations. For example, sentence tokenization breaks down text into individual sentences, while word tokenization breaks down text into individual words. The choice of tokenization method depends on the specific requirements of the NLP task at hand.

Word Tokenization

Word tokenization is the process of splitting a large paragraph into words. The idea here is to split the input into words by identifying certain delimiters. Delimiters are characters that separate words, such as spaces, punctuation marks, and newline characters. Word tokenization is often used in tasks like word count or when preparing text for further processing in NLP.

It’s important to note that word tokenization can be complex in languages that do not use spaces between words, or in texts where punctuation is used inconsistently. In such cases, advanced tokenization methods, such as statistical tokenization or rule-based tokenization, may be used.

Sentence Tokenization

Sentence tokenization, also known as sentence segmentation, is the process of dividing text into individual sentences. Unlike word tokenization, which uses spaces and punctuation as delimiters, sentence tokenization requires a deeper understanding of the text to accurately identify the end of a sentence. This is because a period can denote the end of a sentence, but it can also be used in abbreviations, decimals, and dates.

Sentence tokenization is often used in tasks that require understanding the context of sentences, such as sentiment analysis or text summarization. It is also a crucial step in building a document-term matrix (DTM), which is used in many NLP tasks.

Stop Words Removal

Stop words are words that are filtered out before or after text processing in NLP. These are typically words that are very common and carry little meaningful information. Examples of stop words in English include ‘is’, ‘an’, ‘the’, ‘and’, and ‘in’. Removing stop words can significantly reduce the size of the data to be processed, thereby improving the performance of different NLP algorithms.

However, it’s important to note that the list of stop words can vary depending on the context. For example, in sentiment analysis, words like ‘not’ may carry significant meaning and should not be removed. Therefore, the choice of stop words can have a significant impact on the results of an NLP task.

Standard Stop Words

Standard stop words are the most common words in a language. These words are often removed from the text data because they occur so frequently that they may not provide valuable information for analysis. In English, standard stop words include ‘a’, ‘an’, ‘the’, ‘and’, ‘is’, ‘it’, and ‘that’, among others.

Most NLP libraries, such as NLTK and SpaCy, provide a list of standard stop words that can be used to filter out these words from the text data. However, it’s important to review this list and modify it as necessary based on the specific requirements of the NLP task.

Custom Stop Words

Custom stop words are words that are identified as stop words based on the specific requirements of an NLP task. These could be words that are very common in the text data but do not provide any valuable information for analysis. For example, in a text analysis of tweets, words like ‘RT’ (retweet) and ‘via’ could be considered as custom stop words because they do not provide any meaningful information.

Identifying custom stop words requires a good understanding of the text data and the NLP task. It often involves an iterative process of text analysis and refining the list of custom stop words. This process can significantly improve the performance of the NLP algorithm and the quality of the analysis results.

Stemming and Lemmatization

Stemming and lemmatization are text normalization techniques used in NLP to reduce inflectional forms and sometimes derivationally related forms of a word to a common base form. These techniques are used to reduce the dimensionality of the data and to improve the performance of NLP tasks that involve text matching, such as search and information retrieval.

While stemming and lemmatization serve similar purposes, they do so in different ways. Stemming typically refers to a crude heuristic process that chops off the ends of words in the hope of achieving this goal correctly most of the time. Lemmatization, on the other hand, takes into consideration morphological analysis of the words and is therefore more accurate and sophisticated.


Stemming is a process where words are reduced to their root form. This is done by removing the derivational affixes. For example, the stem of the words ‘jumping’, ‘jumps’, ‘jumped’ is ‘jump’. Stemming helps in reducing the corpus of words the model is exposed to, and explicitly correlates words with similar meaning.

There are several algorithms available for stemming such as the Porter Stemmer, Snowball Stemmer, and Lancaster Stemmer. These algorithms have different rules for stemming words, and the choice of algorithm can have a significant impact on the results of the stemming process.


Lemmatization, unlike Stemming, reduces the inflected words properly ensuring that the root word belongs to the language. In Lemmatization, the root word is called Lemma. A lemma (plural lemmas or lemmata) is the canonical form, dictionary form, or citation form of a set of words.

For example, runs, running, ran are all forms of the word run, therefore run is the lemma of all these words. Because lemmatization returns an actual word of the language, it is used where it is necessary to get valid words.

Part of Speech Tagging

Part of Speech (POS) tagging is the process of marking up a word in a text as corresponding to a particular part of speech, based on both its definition and its context. It is a crucial step in the NLP pipeline as it provides a more detailed analysis of the text and enables the development of more sophisticated NLP models.

POS tagging can be used for many NLP tasks like determining correct pronunciation during speech synthesis (for example, ‘discontent’ and ‘discontent’), for information retrieval, and for word sense disambiguation. In POS tagging, different tags are assigned to the words of a sentence depending on their syntactic context and role.

POS Tagging Techniques

There are several techniques used for POS tagging, including rule-based, stochastic, and machine learning techniques. Rule-based techniques use hand-written rules to identify the POS tags of words. Stochastic techniques, on the other hand, use the probability of a sequence of tags occurring in a sentence to identify the POS tags.

Machine learning techniques use algorithms to learn from a pre-tagged corpus of text and then use this learning to tag new sentences. These techniques can be very effective, but they require a large amount of training data and can be computationally intensive.

Applications of POS Tagging

POS tagging has many applications in NLP. It is used in text-to-speech systems to determine the correct pronunciation of words. It is also used in information retrieval systems to improve the precision and recall of search results. In machine translation, POS tagging is used to disambiguate the meaning of words and to improve the quality of translation.

Additionally, POS tagging is used in sentiment analysis to identify the sentiment of a text. For example, adjectives are often used to express sentiment, so identifying adjectives in a text can provide valuable information about the sentiment of the text.

Named Entity Recognition

Named Entity Recognition (NER) is a subtask of information extraction that seeks to locate and classify named entities in text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.

NER is used in many fields in Natural Language Processing (NLP), and it can help answering many real-world questions, such as: Which companies were mentioned in the news article? Were specified products mentioned in complaints or reviews? Does the tweet contain the name of a person? Does the resume mention any organization names?

NER Techniques

There are several techniques used for NER, including rule-based, machine learning, and hybrid techniques. Rule-based techniques use a set of hand-written rules to identify named entities in text. These rules can be based on linguistic patterns, dictionary lookups, and other methods.

Machine learning techniques use algorithms to learn from a pre-tagged corpus of text and then use this learning to identify named entities in new text. These techniques can be very effective, but they require a large amount of training data and can be computationally intensive.

Applications of NER

NER has many applications in NLP. It is used in information retrieval systems to improve the precision and recall of search results. In machine translation, NER is used to identify the names of people, places, and organizations that should not be translated. In sentiment analysis, NER is used to identify the entities being discussed in a text.

Additionally, NER is used in question answering systems to identify the entities in a question and in the potential answers. It is also used in news article classification to identify the entities mentioned in an article and to classify the article based on these entities.


Text analysis techniques in NLP are essential tools for making sense of the vast amounts of unstructured text data that are generated every day. These techniques provide the means to extract valuable insights from text data and to use these insights to make informed decisions.

From tokenization to named entity recognition, each technique plays a crucial role in the NLP pipeline. Understanding these techniques and how they work is the first step towards mastering the field of NLP. As the field continues to evolve, new techniques and tools are being developed to further enhance our ability to understand and analyze text data.

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