NLP Pipeline: Key Steps to Process Text Data

Natural language processing is a crucial part of modern AI applications. It has significantly improved human-computer interactions in the past few years. From chatbots to sentiment analysis, NLP plays an important role in allowing you to have seamless interactions with machines. The results produced by an NLP model rely on the specific pipeline used to develop the model. Understanding every component of an NLP pipeline is necessary before tackling real-world problems.

In this article, you will learn about all the key steps to process text data using an NLP pipeline. You will also look into how to build a pipeline that allows you to analyze sentiments from a social media application like X, formerly Twitter.

What Is Natural Language Processing (NLP)?

Natural Language Processing, or NLP, is a subset of artificial intelligence (AI) that provides computers the ability to understand, process, and interpret human language. NLP relies on computational linguistics, statistical modeling, and machine learning algorithms to analyze and thoroughly communicate in natural languages.

For example, you can set up an alarm clock on your phone using a virtual assistant like Siri. It relies on natural language processing to break down your speech into components that your device can understand and conduct tasks accordingly.

NLP Pipeline - What You Need to Know About It

The NLP pipeline comprises numerous steps, each representing a specific function to enhance a computer's semantic understanding. However, machines do not generally understand the unstructured data generated and acquired via real-world scenarios, so processing steps are necessary for natural language processing pipelines.

Let’s dive deeper into the different components of NLP pipelines.

Data Acquisition

Data can reside in numerous sources, including databases, data lakes, web pages, or publicly available forums. Acquiring this data is the initial step of any NLP pipeline; it involves extracting data from many locations and consolidating it into a single repository. By storing the data in a single source of truth, you can enhance its accessibility for NLP tasks.

However, centralizing data can be difficult, requiring you to ensure data consistency and manage scalability challenges. To overcome this complexity, you can incorporate data integration tools like Airbyte into your workflow.

Airbyte is a no-code tool that enables you to migrate data between diverse platforms. It offers more than 550 pre-built connectors to move structured, semi-structured, and unstructured data to your preferred destination. If the connector you seek is unavailable, Airbyte provides a Connector Builder and a suite of Connector Development Kits (CDKs) to build custom connectors.

Let’s explore a few features offered by Airbyte:

• AI-Powered Connector Builder: The Connector Builder comes with an AI assistant that reads through your connector’s API documentation and auto-fills most configuration fields. This simplifies your connector development process.‍
• Developer-Friendly Pipelines: PyAirbyte—a Python library—allows you to use Airbyte connectors in a Python development environment. Utilizing this library, you can extract data from numerous sources into prominent SQL caches, such as Postgres, Snowflake, and BigQuery. PyAirbyte cached data is compatible with Python libraries like Pandas and AI frameworks like LangChain and LlamaIndex.‍
• Change Data Capture (CDC): With Airbyte’s CDC feature, you can identify and replicate source data changes to the destination system. This functionality allows you to keep track of updates and maintain data consistency.‍
• Web Scrapper Connector: Airbyte supports a Web Scrapper connector. Using the combination of Airbyte and Web Scrapper, you can scrape data from web pages and integrate it into an analytics environment.‍
• Automated RAG Techniques: With automated chunking, embedding, and indexing operations, you can transform raw data into vector embeddings and store them in prominent vector databases like Pinecone. This process can streamline the development of robust AI applications.

Data Processing

After extracting data, the next critical step is to perform basic processing to refine the raw data so that it can be used for further analysis. This phase of the NLP pipeline involves data cleaning and processing.

NLP data generally comprises textual formats, which might contain values that are not essential for analysis. Eliminating unnecessary data reduces the storage space and processing time. For instance, the extracted text data might contain emoticons and HTML tags that can be removed.

Another cleaning step is to perform rudimentary spelling checks. This is important because it enables better context associations with specific words.

For custom modifications, you can integrate Airbyte with the dbt, a data transformation tool. This integration allows you to transform and enrich data based on your needs. You can also leverage PyAirbyte to migrate the data to a Python development environment where you can perform custom transformations.

Upon cleaning the data, you can perform the processing steps. Some basic data processing components of an NLP pipeline are tokenization, stopword removal, lemmatization/stemming, and language detection.

• Tokenization assists in breaking larger text datasets into smaller, more manageable chunks that are easier to analyze.
• Stopword removal involves discarding words, such as the, is, and an, that do not contribute to the analysis.
• When dealing with multilingual data, it becomes necessary to identify the language used in the text before analysis. Libraries like LangDetect can help automate this process.
• Lemmatization and stemming are two processes that reduce long-form words to their root form. For example, the word transferring can become transfer.

In the next step, advanced processing, like Part-of-Speech (PoS) tagging, coreference resolution, and named entity recognition (NER), can be useful.

• To provide insights about the syntax of the textual data, PoS tagging enables you to assign grammatical categories—nouns, adjectives, and verbs—to the text.
• Coreference resolution helps in linking similar expressions in a text. For example, in the sentence “Tom listens to music. He loves Jazz,” the words “Tom” and “He” refer to the same person, Tom.
• Named Entity Recognition encourages you to identify and classify named entities, like the name of an organization, person, or location. This is an essential component for tasks like information retrieval, machine translation, and query resolution.

Feature Engineering

Feature engineering is the process of transforming data into features that machine learning models can utilize to generate responses. In terms of an NLP pipeline, this refers to converting textual data into numeric formats, enabling machine learning models to process the information effectively. This supports capturing the semantic meaning of the data and the relationships between words.

Here are some techniques to transform text data into numeric values:

Bag of Words (BoW): It is the process of representing text as a collection of words in a multidimensional space. The number of dimensions is equal to the number of unique words in the dataset. Each vector in this multidimensional space represents a collection of words or a document. Although this method is reliable, it does not preserve the sequence of words required to recreate the original sentence.‍

N-Gram Model: The n-gram model captures the sequence of n consecutive words. For example, representing the sentence “I love watching football” as a unigram sequence results in a list containing “I”, “love”, “watching”, and “football.” In Bigram, the word sequence becomes “I love”, “love watching”, and “watching football.” The n-gram model predicts the likelihood of words that can come after a sequence of words.

‍Term Frequency-Inverse Document Frequency (TF-IDF): TF-IDF is a statistical method of measuring the importance of a term within a document relative to a collection of documents or a corpus. The words in the document are converted into numeric values, highlighting their importance in terms of context.‍

Word Embeddings: Word2Vec, FastText, and GloVe are some of the various techniques for representing a word as a vector in a continuous vector space. The words or phrases with a high correlation of simultaneous occurrence are positioned closer in this multidimensional space. In modern data stacks, transformer-based systems, like BERT vector embeddings, are becoming prevalent. These models utilize existing LLMs to convert words into embeddings.

Model Development

The most crucial component of any NLP pipeline is the model development step. It involves either utilizing a machine learning algorithm or building a deep learning model trained on your data. Let’s explore the approaches that you can take to develop an NLP model.

Heuristic-Based Approach

The heuristic approach provides a practical solution to complex NLP problems. Although not fully optimized, it offers a balance between computational efficiency and acceptable solution quality in a reasonable amount of time. This approach depends on predefined rules to enable the management of specific tasks, like sentiment analysis. Regular expressions, or RegEx, are prominently used in heuristic-based approaches to identify data patterns, such as detecting suspicious domains using alphanumeric characters.

Machine Learning Approach

Machine learning algorithms, such as Naive Bayes, Support Vector Machines, and Random Forests, can support you in analyzing training data to produce predictions. You can use libraries like scikit-learn to implement these algorithms almost effortlessly, avoiding the development of an ML model from scratch. Utilizing this library, you can use an algorithm with your processed data so that it can produce best-fitting results.

Deep Learning Approach

Deep learning models use neural networks with multi-layered architecture to identify patterns in the training data. These patterns allow the models to produce output responses to unfamiliar queries. For example, Recurrent Neural Networks, or RNNs, are beneficial for modeling sequential data, as they consider current and historical data input when processing information.

Cloud-Based APIs

Cloud-based APIs offer pre-built models that can cater to large-scale NLP tasks. These models save the time and resources required to build a model from scratch. Some of the most commonly used cloud-based APIs include Google Cloud Natural Language API, IBM Watson Natural Language Understanding, and Azure Text Analytics API. You can integrate these APIs into your NLP pipeline to perform tasks like sentiment analysis.

Model Evaluation

Model evaluation aids in determining the model performance and whether it can be deployed to the production environment. You can perform the evaluation phase of the NLP pipeline through intrinsic or extrinsic methods.

The intrinsic evaluation of the model involves examining its performance in isolation, disregarding real-world application. This form of evaluation has various metrics to consider when inspecting the model's efficiency. Some metrics include accuracy, precision, F1 score, BiLingual Evaluation Understudy (BLEU) score, and perplexity.

On the other hand, extrinsic evaluation of the model enables you to measure the model’s effectiveness in terms of real-world applications. This process involves testing the model’s response for metrics like customer satisfaction score, user engagement rates, and revenue impact.

Model Deployment

Depending on the model’s performance in the evaluation phase, it can either be fine-tuned or deployed in the production environment. The deployment step involves the transition of the NLP model to an environment where it is open to feedback from the end users.

To ensure the expected performance of the model in the long haul, you must continuously monitor and update it based on constructive feedback. The monitoring and updating phases of deployment are continuous processes essential for maintaining the model’s relevance.

How to Build an NLP Pipeline with PyAirbyte—Real-World Use Case

This section thoroughly describes a real-world scenario for analyzing text data from a social networking platform, X (formerly Twitter). By building an NLP pipeline that extracts data from Twitter, you can analyze user sentiments about trending topics.

To start with the steps, you must ensure that all the required prerequisites are satisfied.

Prerequisites

• Access to a code editor like Jupyter Notebook to execute the code snippets.
• Necessary credentials to extract data from Twitter. For more information, read Airbyte’s official Twitter documentation.

Step 1: Installing Dependencies

The first step involves installing all the required libraries for building the pipeline.

To install PyAirbyte, execute:

pip install airbyte

Since we will create a sentiment analyzer, let’s install the vaderSentiment library. Valence Aware Dictionary and sEntiment Reasoner, or VADER, is a rule-based sentiment analysis tool optimized for analyzing the emotions involved in social media platforms. Execute the following code:

pip install vaderSentiment

Step 2: Set Up Twitter as a Data Source

Import the libraries installed in the previous step.

import airbyte
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
import pandas as pd

Before establishing a connection, define the configuration credentials to access data from Twitter. Replace your_access_token, your_access_token_secret, your_consumer_key, and your_consumer_secret placeholders with your credentials in the code below:

twitter_config = {
    "credentials": {
        "access_token": "your_access_token",
        "access_token_secret": "your_access_token_secret",
        "consumer_key": "your_consumer_key",
        "consumer_secret": "your_consumer_secret",
    },
}

Now, to establish a connection to Twitter, execute the following code:

source = ab.get_source(
    "source-twitter",
    config=twitter_config,
    install_if_missing=True,
)

In the above code, the get_source method supports setting up the source connector as Twitter.

To verify if the connection is correctly set up, run:

source.check()

This connection can have multiple data streams, including ads, tweets, and search queries. You can check all the available streams with this code:

source.get_available_streams()

Let’s select all the available data streams in our NLP pipeline. To achieve this, execute:

source.select_all_streams()

Step 3: Extract Data into a Local Cache

The data streams are now available for processing. However, it is beneficial to store the data in a local cache for temporary storage while you perform the analysis. PyAirbyte supports the DuckDB cache by default. To retrieve the data in the cache, run the following:

cache = ab.get_default_cache()
result = source.read(cache=cache)

Step 4: Data Processing

You can now process this data into a structured format for analysis. This assists in ensuring that the data is clean, consistent, and standardized. One of the preferable ways to do this is to use Pandas DataFrame data structure, which represents data in a tabular format. To transform a particular stream of data into a DataFrame, replace your_stream placeholder with the specific data stream’s name while executing the following:

df = cache["your_stream"].to_pandas()

Now that the data is available in a row-column format, you can perform transformations to modify it into an analysis-ready format, depending on your requirements. For this step, you can install numerous libraries, like Natural Language Toolkit (NLTK), to perform text analysis in Python.

Step 5: Performing Sentiment Analysis on the Data

The final step includes defining a pipeline text classification method to analyze the sentiments of tweets left by the users. A rule-based sentiment analysis tool like VADER can enable seamless social media sentiment analysis. Let’s use the SentimentIntensityAnalyzer class to identify the emotions behind tweets.

Create a SentimentIntensityAnalyzer object.

sid_obj = SentimentIntensityAnalyzer()

The polarity_scores method of the SentimentIntensityAnalyzer class provides you with a detailed description of the text data. It outputs compound scores that help determine whether the user response is positive, negative, or neutral.

sentiment_dict = sid_obj.polarity_scores(sentence)

To check the output results, execute the code below:

print("Overall sentiment dictionary is: ", sentiment_dict)
print("Sentence was rated as", sentiment_dict['neg']*100, "% Negative")
print("Sentence was rated as", sentiment_dict['neu']*100, "% Neutral")
print("Sentence was rated as", sentiment_dict['pos']*100, "% Positive")
print("Sentence Overall Rated As", end=" ")

You can also create a conditional statement to identify if the sentiment of the tweet is positive, negative, or neutral.

if sentiment_dict['compound'] >= 0.05:
    print("Positive")
elif sentiment_dict['compound'] <= -0.05:
    print("Negative")
else:
    print("Neutral")

The range of compound score is between -1 to +1, with

• A negative score below -0.05.
• A positive score above +0.05.
• A neutral score between -0.05 and +0.05.

You can further visualize this information using dashboards with libraries like Matplotlib and Seaborn. Another beneficial step that you can take is to create a chatbot that responds to your queries according to a specific dataset. To accomplish this, you can integrate existing large language models, like LangChain’s ChatOpenAI, in the same Python environment. Follow this Airbyte GitHub repository to learn more about building a chatbot.

Role of HuggingFace in NLP Pipelines

HuggingFace is a leading platform in the domain of natural language processing. It offers a collection of pre-trained models to conduct NLP tasks seamlessly. Utilizing these models, you can automate multiple processes, such as text processing and sentiment analysis, within your NLP pipeline.

Among the common NLP tasks that can be automated using HuggingFace is segmentation, which involves breaking down complex text into smaller components. In an NLP pipeline, segmentation involves sentence, morphological, and topic segmentation, along with tokenization. HuggingFace has various models to perform text segmentation, including:

• GPT: Generative Pre-trained Transformer, or GPT, is a pre-trained unidirectional transformer that uses language modeling of textual data with long-range dependencies. To facilitate LLM tokenization, this transformer supports the OpenAIGPTTokenizer class.‍
• BERT: Bidirectional Encoder Representations from Transformers, or BERT, is a bidirectional transformer. It is trained using a fusion of masked language modeling and next-sentence prediction on a large corpus. Using its pre-trained BERTTokenizer class, you can split complex text documents into smaller components.‍
• RoBERTa: Built upon Google’s BERT model, a Robustly Optimized BERT Pretraining Approach, or RoBERTa, is an enhanced HuggingFace model. It has modified hyperparameters, which eliminate the next-sentence pretraining objective with larger mini-batches and higher learning rates. For this model, you can utilize the RobertaTokenizer to split larger texts.‍
• T5: Text-to-Text Transfer Transformer, or T5, is an encoder-decoder model that converts all the NLP tasks into text-to-text format. It is trained using teacher forcing, which pairs input sequences with corresponding target sequences for training. For text segmentation, you can use the T5Tokenizer class.‍
• DistilBERT: As a distilled version of the BERT model, the DistilBERT model offers a smaller, faster, and cheaper alternative to the original model. Compared to Google BERT, it has 40% fewer parameters and provides 60% faster performance. To tokenize the data with DistilBERT, use the DistilBertTokenizer.

Let’s say you intend to learn how HuggingFace optimizes NLP tasks. To perform sentiment analysis with the DistilBERT model, you can use the code below:

import torch
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification

tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits

predicted_class_id = logits.argmax().item()
model.config.id2label[predicted_class_id]

Executing the above code allows you to perform sentiment analysis on any data using the DistilBERT model fine-tuned on the SST-2 dataset. This method provides a compact way to perform the NLP pipeline operations, saving time and resources required to build models. You can also create a function that uses the above code to analyze the sentiments of the X data extracted from PyAirbyte.

Key Takeaways

NLP pipelines comprise various phases of building applications that can allow machines to interact with humans. By designing custom pipelines, you can streamline numerous business processes, such as customer interaction.

Although there are multiple ways to create solutions that cater to your business, platforms like Airbyte and HuggingFace can simplify complex procedures. By utilizing the features offered by these platforms, you can accelerate data acquisition and transformation, as well as model development and deployment.