The FreeTxt app

Here is the link to FreeTxt app which is currently under development.

• FreeTxt was developed as part of an AHRC funded collaborative FreeTxt supporting bilingual free-text survey and questionnaire data analysis research project involving colleagues from Cardiff University and Lancaster University (Grant Number AH/W004844/1).

• The team included PI - Dawn Knight; CIs - Paul Rayson, Mo El-Haj; RAs - Nouran Khallaf, Ignatius Ezeani and Steve Morris. The Project Advisory Group included representatives from: National Trust Wales, Cadw, Museum Wales, CBAC | WJEC and National Centre for Learning Welsh.

• For further information on the FreeTxt project please contact the project team: CorCenCC@Cardiff.ac.uk

FreeTxt-Flask Setup Instructions

Follow these steps to set up the project on your local machine.

Prerequisites

Before you start, make sure you have installed:

• Git
• Python 3
• pip (Python package installer)

Step 1: Clone the Repository

Clone the FreeTxt-Flask repository from GitHub to your local machine:

git clone https://github.com/UCREL/FreeTxt-Flask.git
cd FreeTxt-Flask

Step 2: Set Up a Python Virtual Environment

It's recommended to use a Python virtual environment for project dependencies to avoid conflicts with other projects or system-wide Python packages.

Create a Virtual Environment

For Unix/Linux/macOS:

python3 -m venv venv

For Windows:

python -m venv venv

Activate the Virtual Environment

On Unix/Linux/macOS:

source venv/bin/activate

On Windows (Command Prompt):

.\venv\Scripts\activate

On Windows (PowerShell):

.\venv\Scripts\Activate.ps1

Step 3: Install Project Dependencies

With your virtual environment activated, install the project dependencies using:

pip install -r requirements.txt

Step 4: Configure the Flask Application

Inform Flask about the entry point of your application by setting the FLASK_APP environment variable:

Unix/Linux/macOS:

export FLASK_APP=main.py

Windows (Command Prompt):

set FLASK_APP=main.py

Windows (PowerShell):

$env:FLASK_APP = "main.py"

Step 5: Run the Flask Application

Now you're ready to run the application:

flask run

This will start a local web server. By default, the Flask application will be accessible at http://127.0.0.1:5000/ from your web browser.

---

SentimentAnalyser Class

Overview

SentimentAnalyser is a Python class that leverages a pre-trained BERT model for sentiment analysis on textual data. It can process texts in different languages and is equipped to handle both English and Welsh languages specifically.

Initialisation

Initializes the sentiment analysis model and tokenizer upon instantiation.

Methods

preprocess_text(text)

• Purpose: Prepares the text for sentiment analysis by removing URLs, mentions, hashtags, punctuation, converting to lowercase, and removing stopwords.
• Parameters:
  • text (str): The text string to preprocess.
• Returns: Preprocessed text.

analyse_sentiment(input_text, language, num_classes, max_seq_len=512)

• Purpose: Conducts sentiment analysis on the input text.
• Parameters:
  • input_text (str): The text to analyze.
  • language (str): The language of the text ('en' for English, 'cy' for Welsh).
  • num_classes (int): Number of sentiment classes (3 or 5).
  • max_seq_len (int, optional): Maximum sequence length for tokenization.
• Returns: A tuple containing the sentiments and sentiment counts.

generate_scattertext_visualization(dfanalysis, language)

• Purpose: Generates a scattertext visualization based on sentiment analysis results.
• Parameters:
  • dfanalysis (pd.DataFrame): DataFrame containing the sentiment analysis results.
  • language (str): The language for the visualization.
• Returns: A path to the generated scattertext visualization HTML file.

This class is suitable for projects requiring detailed sentiment analysis and visualization, especially in bilingual contexts.

KWICAnalyser Class

Overview

KWICAnalyser is a Python class tailored for Keyword-in-Context (KWIC) analysis, semantic tagging, and collocation analysis in textual data. It offers comprehensive functionality for textual analysis, especially useful in linguistic and semantic studies.

Initialisation

Initializes the class with either a text string or a pandas DataFrame. It preprocesses the text, performs semantic tagging, and prepares it for further analysis.

Methods

get_kwic(keyword, window_size, max_instances, lower_case, by_tag, by_sem)

• Purpose: Performs KWIC analysis for a given keyword.
• Parameters:
  • keyword (str): The target keyword.
  • Other parameters for configuring the analysis.
• Returns: A list of KWIC instances.

get_top_n_words(remove_stops, topn)

• Purpose: Retrieves the top N frequent words from the text.
• Parameters: Options to remove stopwords and specify the number of words.
• Returns: A list of the most common words.

get_collocs(kwic_insts, topn)

• Purpose: Finds collocations from the KWIC instances.
• Parameters: KWIC instances and the number of top collocations.
• Returns: A list of top collocations.

plot_coll_14(keyword, collocs, output_file)

• Purpose: Creates a network graph visualization for collocations.
• Parameters: Keyword, collocations, and output file name.
• Returns: Path to the generated network graph.

get_collocation_strength(keyword, topn, window_size, by_tag, by_sem)

• Purpose: Computes collocation strength using mutual information and log-likelihood.
• Parameters: Keyword and configuration for collocation analysis.
• Returns: A list of collocations with their strength scores.

tag_semantics(text)

• Purpose: Tags the input text with semantic tags.
• Parameters: The text to be tagged.
• Returns: A list of tokens with semantic tags.

get_sorted_unique_tags()

• Purpose: Retrieves a sorted list of unique semantic tags.
• Returns: A sorted list of unique tags.

get_word_frequencies()

• Purpose: Computes the frequency of each word in the text.
• Returns: A dictionary of word frequencies.

This class is essential for in-depth text analysis, providing tools for KWIC analysis, collocation strength measurement, and semantic tagging, making it particularly valuable in linguistic research and text processing applications.

WordCloudGenerator Class

Overview

WordCloudGenerator is a Python class for creating visually appealing word clouds with custom shapes, colors, and filtering options. It is capable of semantic tagging, handling different languages, and applying various statistical measures for word selection.

Initialisation

• Initializes with input_data, which can be a DataFrame or raw text.
• Sets up stopwords, punctuation, and USAS tags list.
• Prepares the text and semantic tags for word cloud generation.

Methods

load_image(image_file)

• Loads an image to be used as a mask for the word cloud.

preprocess_data(data, language)

• Preprocesses the input data based on the specified language.

Pymsas_tags(text)

• Tags the text using the Pymsas API for semantic analysis.

calculate_measures(df, measure, language)

• Calculates statistical measures like Keyness and log-likelihood for words.

filter_words(word_list)

• Filters out stopwords and punctuation from the word list.

get_wordcloud(dataframe, metric, word_list, cloud_shape_path, cloud_outline_color, cloud_type)

• Generates a word cloud with custom settings and filtering based on the provided parameters.

compute_word_frequency(tokenized_words, language)

• Computes the frequency of words in the tokenized text.

generate_wordcloud_type(input_data, cloud_type, language, cloud_measure, wordlist=None)

• Generates a word cloud of a specific type (e.g., nouns, verbs, semantic tags) based on the language and measure.

generate_wordcloud(cloud_shape_path, cloud_outline_color, cloud_type, language, cloud_measure, wordlist={})

• Controls the word cloud generation process, integrating all functionalities to produce the final word cloud.

This class is particularly useful for linguistic and textual data visualization, offering versatile word cloud generation capabilities for a wide range of applications.

Summariser class

run_summarizer Function Overview

The run_summarizer function is designed to provide a concise summary of a given text using the TextRank algorithm. This function is versatile, as it can accept both strings and iterable objects as input. If the input is not a string, the function converts it into one. The core of this function lies in its ability to adjust the length of the summary based on the chosen_ratio parameter, which dictates the proportion of the original text to be included in the summary.

Key Features

• Input Flexibility: Handles both string and iterable inputs, seamlessly converting iterables into a string format.
• Customizable Summary Length: Allows the user to specify the summary length through the chosen_ratio parameter, with a minimum threshold set at 0.1.
• Language Support: Primarily supports English (default) and welsh but can be adapted for other languages.

LanguageChecker Class

Overview

LanguageChecker is a Python class designed for efficient language detection and segregation within pandas dataframes. It is particularly useful for processing multilingual text datasets and can specifically handle English and Welsh texts.

Initialisation

• Dataframe (data): The pandas dataframe to be processed.
• Column Name (column): The name of the column in the dataframe containing the text.

Methods

detect_language_file(text)

• Purpose: Identifies the language of a given text string.
• Parameters:
  • text (str): The text string for language detection.
• Returns: A language code (e.g., 'en', 'cy') if successful, or None if the detection fails.

detect_and_split_languages()

• Purpose: Enhances the dataframe by adding a language identification column and then segregates the data into separate dataframes based on the detected languages.
• Returns: A tuple of two dataframes, one with all English texts ('en') and the other with all Welsh texts ('cy'), if present.

This class is an essential tool for projects dealing with bilingual or multilingual datasets, offering streamlined processing for language-specific analysis or operations.

Contacts

• Nouran Khallaf
• Ignatius Ezeani
• Paul Rayson
• Mahmoud El-Haj
• Dawn Knight

• This work with all the accompanying resources is licensed under a Creative Commons Attribution 4.0 FreeTxt is licensed under Apache 2.0 and thus is freely available for use by professional communities and individuals with an interest in language analysis.

Citation

If you use FreeTxt in your research or project, please cite it as follows:

Khallaf, N., Ezeani, I., Knight, D., Rayson, P., El-Haj, M. and Morris, S. (2023). FreeTxt – A Bilingual Free-Text Analysis and Visualisation Toolkit [Software]. Cardiff University and Lancaster University. Available at: www.freetxt.app