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Transformers vs. spaCy? Leveraging NLP for Effective Reddit Analysis

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This article series explored techniques for analyzing and extracting insights from Reddit data focusing on investment discussions. We began by establishing a workflow for identifying frequently mentioned organizations within a Reddit investing subreddit. This involved data cleaning, entity recognition using spaCy, and the creation of blacklists to exclude irrelevant entities.

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SpaCy is a popular library for named entity recognition (NER) and other natural language processing (NLP) tasks. It’s known for its ease of use and effectiveness. NER is the process of automatically extracting specific entities from text, such as people, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.

This article provides a step-by-step guide on how to use spaCy for NER.

First, you need to install spaCy using pip install spacy.

Then, you can import spaCy and load a model. spaCy models are not downloaded by default, so you’ll need to use the spacy download command to download a model before you can use it.

Models are identified by language, model type, genre, and size. The genre can be web or news, and the size can be small, medium, or large.

Once you’ve loaded a model, you can use it to process text. spaCy’s output is a document object that contains the extracted entities and other metadata.

You can visualize the entities using spaCy’s display module. This is a great way to get a sense of what entities the model is identifying in your text.

Extracting Entities with spaCy

In this article, we visualized the entities extracted from a text using spaCy’s display module. Now, we’ll take a closer look at the different entity types and how to programmatically extract them from the text.

Understanding Entity Types

spaCy assigns different labels to entities based on their type. For example, the label ORG indicates an organization, while GPE indicates a geopolitical entity (country, city, state, etc.).

To understand the meaning of a specific label, you can use spaCy’s explain function. For instance, to understand the ORG label, you would run:

Python

spacy.explain(“ORG”)

This will print a definition of the label.

Extracting Specific Entities

To extract specific entities from the text, you can iterate over the entities in the document object and filter them based on their labels.

For example, to extract all organizations from the text, you would use the following code:

Python

orgs = []

for ent in doc.ents:

    if ent.label_ == “ORG”:

        orgs.append(ent.text)

This code creates a list (orgs) and adds the text of each entity with the label ORG to the list.

Example: Extracting Organizations from a Text

Let’s consider the following text:

In this article, we’ll explore how to use spaCy for named entity recognition (NER). spaCy is a popular library for NER and other natural language processing (NLP) tasks. It’s known for its ease of use and effectiveness.

To extract all organizations from this text, you would run the following code:

Python

import spacy

# Load the spaCy model

nlp = spacy.load(“en_core_web_sm”)

# Process the text

doc = nlp(“In this article, we’ll explore how to use spaCy for named entity recognition (NER). spaCy is a popular library for NER and other natural language processing (NLP) tasks. It’s known for its ease of use and effectiveness.”)

# Extract organizations

orgs = []

for ent in doc.ents:

    if ent.label_ == “ORG”:

        orgs.append(ent.text)

# Print the extracted organizations

print(orgs)

This code will print the following output:

[‘spaCy’]

Exercise: Extracting Entities from a Different Text

Use the same steps as above to extract all entities and their corresponding labels from the following text:

Apple is an American multinational technology company headquartered in Cupertino, California, that designs, develops, and sells consumer electronics, computer software, and online services. It is considered one of the Big Five technology companies along with Amazon, Google, Microsoft, and Meta.

Apple was founded by Steve Jobs, Steve Wozniak, and Ronald Wayne in 1976. The company’s first product, the Apple I, was released in 1976. The Apple II, released in 1977, was a success that established Apple as a major player in the personal computer market.

In the 1980s, Apple introduced a graphical user interface (GUI) with the release of the Macintosh computer in 1984. The Macintosh was followed by a series of successful products, including the iMac and iPod.

In the 2000s, Apple introduced the iPhone, which revolutionized the mobile phone industry. The iPad, released in 2010, was another major success for the company.

Apple is now one of the most valuable companies in the world. Its products are popular among consumers and businesses alike.

Solution: Extracting Entities from the Example Text

Here’s the code to extract all entities and their corresponding labels from the example text:

Python

import spacy

# Load the spaCy model

nlp = spacy.load(“en_core_web_sm”)

# Process the text

doc = nlp(“Apple is an American multinational technology company headquartered in Cupertino, California, that designs, develops, and sells consumer electronics, computer software, and online services. It is considered one of the Big Five technology companies along with Amazon, Google, Microsoft, and Meta.

Apple was founded by Steve Jobs, Steve Wozniak, and Ronald Wayne in 1976. The company’s first product, the Apple I, was released in 1976. The Apple II, released in 1977, was a success that established Apple as a major player in the personal computer market.

Retrieving Posts from a Reddit Subreddit using Reader API

This article explores how to retrieve posts from a specific subreddit using the Reddit Reader API.

Prerequisites

  • Familiarity with the Reader API, including access and authentication methods.
  • Understanding of JSON data format.
  • Pandas library for data manipulation.

Steps

  • Define the API Address:
    • Set up a variable to store the base URL for the Reader API endpoint.
  • Retrieve Posts:
    • Use requests.get with the API address and subreddit name as parameters.
    • Include headers for authentication.
    • Extract the data from the JSON response.
  • Extracting Post Information:
    • Access the “children” list within the JSON response, containing individual posts.
    • Each post entry includes details like title, selftext (content), subreddit, creation time (created_utc), upvotes, downvotes, and score.
  • Creating a Pandas Dataframe:
    • Import the pandas library.
    • Define a dataframe with columns for desired post information (name, created_utc, subreddit, title, selftext, upvote_ratio, upvotes, downvotes, score).
  • Iterating and Appending Data:
    • Loop through each post in the response.
    • Extract the required data points from each post using its data dictionary.
    • Append the extracted data as a new row to the dataframe.
  • Looping Back in Time:
    • Utilize the after parameter in subsequent requests to retrieve older posts.
    • Access the name value of the latest post in the dataframe for this purpose.
  • Implementing an Exit Criteria:
    • Employ a while loop to continuously retrieve posts until no more data is available.
    • Check the length of the response’s “children” list to determine if the API has reached its limit.
  • Saving the Dataframe:
    • Replace any pipe characters in the dataframe to avoid conflicts with the chosen delimiter.
    • Use df.to_csv to save the dataframe as a CSV file, specifying the filename, delimiter (pipe character), and setting index=False to exclude the index column.

This process allows you to efficiently extract and store post data from a chosen Reddit subreddit using the Reader API and Pandas for data manipulation. The retrieved data can be further analyzed for sentiment analysis or other use cases.

Tagging Organizations from Reddit Investing Subreddit with spaCy

This article demonstrates how to extract and identify organizations from a Reddit investing subreddit dataset using spaCy for named entity recognition (NER).

Prerequisites

  • spaCy library
  • pandas library
  • CSV file containing Reddit investing subreddit data (named “Reddit_investing.csv”)

Steps

  • Import Libraries and Load spaCy Model:
    • Import spaCy and pandas libraries.
    • Load the spaCy en_core_web_sm model (small English model).
  • Read CSV Data:
    • Use pandas read_csv function to read the “Reddit_investing.csv” file, specifying the pipe character (“|”) as the delimiter.
  • Define Entity Extraction Function:
    • Create a function named get_all_orgs that takes a string text as input.
    • Use nlp(text) to create a spaCy document object.
    • Initialize an empty list org_list to store identified organizations.
    • Iterate through entities in the document:
      • If the entity label is “ORG” (organization), append the entity’s text to org_list.
    • Convert org_list to a set to remove duplicates and then back to a list.
    • Return the final org_list.
  • Apply Function to Extract Organizations:
    • Add a new column named “organizations” to the dataframe.
    • Use df[‘organizations’] = df[‘selftext’].apply(get_all_orgs) to apply the get_all_orgs function to the “selftext” column (text content) and store the extracted organizations in the “organizations” column.
  • Analyze Extracted Organizations:
    • Print a sample of the dataframe to view identified organizations (e.g., AAC, Citadel, Robinhood).
  • Further Analysis
  • Explore the “organizations” column to understand the prominent organizations mentioned in the Reddit posts.
  • You can potentially filter or group data based on the extracted organizations for further analysis.

This approach effectively utilizes spaCy to extract and label organizations within the Reddit investing subreddit data. This information can be valuable for understanding investment trends, user sentiment towards specific companies, or for further topic modeling tasks.

Analyzing Mention Frequency of Organizations in Reddit Investing Subreddit

This explores techniques to identify the most frequently mentioned organizations from a Reddit investing subreddit dataset.

Prerequisites

  • pandas library
  • collections library

Steps

  • Import Libraries:
    • Import pandas for data manipulation and collections.Counter for frequency analysis.
  • Extract Organizations:
    • Assuming you have a dataframe (df) with a column named “organizations” containing lists of identified organizations for each post.
  • Flatten Nested List:
    • Use df[‘organizations’].tolist() to convert the “organizations” column into a single list containing all organizations mentioned across all posts.
  • Count Organization Mentions:
    • Create a Counter object by passing the flattened list (orgs) to collections.Counter(orgs).
  • Identify Most Frequent Organizations:
    • Utilize the most_common method of the Counter object. Specify the desired number of entries (e.g., org_frequency.most_common(10)) to retrieve the top N most frequently mentioned organizations.
  • Refine Analysis (Optional):
    • Create a blacklist containing irrelevant organizations (e.g., FDA, SEC, New York Stock Exchange).
    • Modify the get_all_orgs function (introduced in previous sessions) to exclude blacklisted entities during organization extraction.

Benefits

  • Understand the organizations most discussed within the Reddit investing community.
  • Gain insights into potential investment trends based on user focus.
  • Identify potential topics for further analysis (e.g., sentiment analysis towards specific companies).

By combining pandas and the Counter class, you can effectively analyze the frequency of organization mentions in your Reddit data. This information can be valuable for understanding the interests and discussions within the investing subreddit community. You can further refine the analysis by excluding irrelevant entities and focusing on organizations relevant to stocks or investments.

Refining Organization Mention Analysis with Blacklist and Sentiment Analysis

This article builds upon previous sessions to refine the analysis of organization mentions in a Reddit investing subreddit dataset.

Challenge

The previous analysis identified irrelevant entities (e.g., FDA, SEC) among frequently mentioned organizations.

Solution: Blacklist and Function Update

  • Create Blacklist:
    • Define a set containing unwanted organizations (e.g., “eve”, “sec”, “new york stock exchange”, “fda”).
  • Update get_all_orgs Function:
    • Modify the conditional statement within the function to exclude blacklisted entities.
    • Convert both the entity text and blacklist items to lowercase for case-insensitive matching.
    • The updated condition: if entity.label_ == “ORG” and entity.text.lower() not in blacklist
  • Re-run Analysis:
    • Reapply the get_all_orgs function to the dataframe’s “organizations” column.
    • Repeat steps for flattening the list of lists, creating a counter, and identifying most frequent organizations.
  • Benefits:
  • Improved accuracy by excluding irrelevant entities.
  • Focusing on stocks and investment-related organizations.

Further Analysis (Optional):

The article concludes by mentioning the possibility of incorporating sentiment analysis into future sessions. This would involve analyzing the sentiment of text surrounding the identified organizations to understand user opinions and potential investment trends.

By implementing a blacklist and refining the get_all_orgs function, you can achieve a more accurate analysis of relevant organizations mentioned in the Reddit data. Further exploration with sentiment analysis can provide deeper insights into user sentiment towards specific companies or stocks.

Sentiment Analysis of Organizations in Reddit Investing Subreddit

This article explores incorporating sentiment analysis into the analysis of organization mentions in a Reddit investing subreddit dataset.

Prerequisites

  • pandas library
  • flair library

Steps

  • Function for Sentiment Analysis:
    • Define a get_sentiment function that takes a text as input.
    • Use flair to tokenize the text and predict sentiment.
    • Extract sentiment direction (positive/negative) and confidence score.
  • Apply Sentiment Analysis:
    • Load the pre-processed dataframe containing the “organizations” column.
    • Create a new “sentiment” column by applying get_sentiment to the “selftext” column.
  • Refine Data for Sentiment Analysis:
    • The “organizations” column stores lists of organizations as strings.
    • Convert these strings back into lists using the ast module.
  • Sentiment Dictionary Creation:
    • Initialize an empty sentiment_dictionary.
    • Iterate through the dataframe:
      • Extract sentiment direction and score from each row.
      • Loop through organizations in the “organizations” column.
        • If the organization exists in the dictionary, append the sentiment score to the appropriate list (positive/negative) within the organization’s entry.
        • If the organization doesn’t exist, initialize a new entry with empty positive and negative score lists, then append the sentiment score.
  • Calculate Average Sentiment Scores:
    • Initialize an average_sentiment list to store sentiment analysis results.
    • Iterate through each organization in the sentiment_dictionary:
      • Calculate positive frequency, negative frequency, and overall frequency.
      • Handle cases with zero entries to avoid division errors.
      • Calculate average positive score, average negative score, and overall average score.
      • Append a dictionary containing the organization, average positive score, average negative score, and overall score to average_sentiment.
  • Convert Sentiment Analysis Results to Dataframe:
    • Create a new dataframe sentiment_df from the average_sentiment list.
  • Filter Low-Frequency Organizations:
    • Filter sentiment_df to keep only organizations with a frequency greater than a specified threshold (e.g., 3).
  • Identify Organizations with Highest/Lowest Sentiment:
    • Sort sentiment_df by the “score” column to identify organizations with the most positive and negative average sentiment.
  • Benefits
  • Understand user sentiment towards different organizations mentioned in the subreddit.
  • Identify potential investment trends based on positive or negative discussions.
  • Limitations
  • Sentiment analysis models may not perfectly capture the nuances of human language.
  • Results should be interpreted with caution and considered alongside other factors.
  • Future Exploration

The article concludes by mentioning the possibility of using a Roberta transformer model for sentiment analysis in the next session. This could potentially improve the accuracy of the sentiment analysis compared to the traditional spaCy model used here.

Leveraging Transformer Models for Entity Recognition with spaCy

 Explore using spaCy with Hugging Face Transformers for named entity recognition (NER).

Prerequisites

  • spaCy library (with Transformers support)

Installation

  • Install spaCy with Transformers:

Bash

pip install spacy[transformers]

  • (Optional) Check CUDA Version (if using CUDA):

Bash

nvcc –version

  • Install Transformer Model (if using CUDA):

Bash

python -m spacy download en_core_web_trf –gpu-accelerator cuda11.1  # Replace cuda version as needed

Downloading Transformer Model

  1. Command Line Download:

Bash

python -m spacy download en_core_web_trf

Using Transformer Model

  1. Import Libraries:

Python

import spacy

from spacy import displacy

  • Load Transformer Model:

Python

nlp_trf = spacy.load(“en_core_web_trf”)

  • Comparison with spaCy Model:
    • Load a larger spaCy model (e.g., en_core_web_lg).
    • Compare entity recognition results on different text samples (simple vs. complex) using displacy.render.

Benefits of Transformer Models

  • Improved performance on complex or longer text sequences compared to traditional spaCy models.

Limitations

  • May not outperform spaCy models on all tasks, especially simpler ones.

We further enhanced the analysis by incorporating sentiment analysis using the Flair library. This allowed us to understand user sentiment towards different organizations mentioned in the subreddit. Finally, we investigated the use of spaCy with Hugging Face Transformers for named entity recognition (NER). Transformer models demonstrated improved performance on complex or lengthy text sequences compared to traditional spaCy models.

These techniques provide valuable tools for gaining insights from social media data like Reddit discussions. By identifying frequently mentioned organizations, understanding user sentiment, and leveraging transformer models for NER, you can extract valuable information to inform investment decisions or research trends. It’s important to remember that sentiment analysis models may not perfectly capture the nuances of human language, and results should be interpreted with caution alongside other factors. This concludes this series on Reddit data analysis for investment discussions.

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