[Advanced] Natural Language Processing: Language Modeling and Sentiment Analysis in MATLAB

# **Advanced Techniques in Natural Language Processing: Language Models and Sentiment Analysis in MATLAB**

## 1. **2.1 Fundamental Concepts of Language Models**

### 2.1.1 Probabilistic Language Models

Probabilistic language models (PLMs) treat language as a series of probabilistic events, predicting the probability of the next word based on the preceding words. They utilize statistical methods to learn the distribution of language from a corpus and model the relationships between words through conditional probability distributions.

### 2.1.2 Neural Language Models

Neural language models (NLMs) are based on neural networks. They use recurrent neural networks (RNNs) or variants such as Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) to learn the context-dependency of language. NLMs capture the complexity and long-term dependencies of language by predicting the distribution of the next word.

## 2. Language Models in MATLAB

### 2.1 Fundamental Concepts of Language Models

#### 2.1.1 Probabilistic Language Models

Probabilistic language models (PLMs) model language as a probability distribution, where the occurrence probability of each word depends on its context. PLMs use statistical techniques to estimate the joint probability of word sequences and use these probabilities to predict the next word.

**Formula:**

P(w_1, w_2, ..., w_n) = P(w_1) * P(w_2 | w_1) * ... * P(w_n | w_1, w_2, ..., w_{n-1})

Where:

* `P(w_1, w_2, ..., w_n)`: Joint probability of the word sequence `w_1, w_2, ..., w_n`
* `P(w_1)`: Probability of the word `w_1`
* `P(w_2 | w_1)`: Conditional probability of the word `w_2` given the word `w_1`

#### 2.1.2 Neural Language Models

Neural language models (NNLMs) are a type of PLM that uses neural networks to learn language models. NNLMs use RNNs or Transformer neural networks to capture long-term dependencies in word sequences.

The advantage of NNLMs lies in their ability to learn complex linguistic features, such as syntax and semantics, and to generate more fluent and natural text.

### 2.2 Language Model Toolbox in MATLAB

MATLAB offers the `Text Analytics Toolbox`, which includes functions for creating, training, and evaluating language models.

#### 2.2.1 Creation and Training of Language Models

% Create a language model
lm = languageModel('nGram', 3);

% Train the language model
train(lm, 'trainData.txt');

**Parameter Explanation:**

* `nGram`: Specifies the n-gram language model, where `n` represents the number of words considered.
* `trainData.txt`: Text file used to train the language model.

#### 2.2.2 Evaluation and Application of Language Models

% Evaluate the language model
perplexity = perplexity(lm, 'testData.txt');

% Use the language model to predict the next word
nextWord = predict(lm, 'I love');

**Parameter Explanation:**

* `testData.txt`: Text file used to evaluate the language model.
* `perplexity`: A metric that measures the performance of a language model; the lower the better.
* `'I love'`: The word sequence for which the next word is to be predicted.
* `nextWord`: The predicted next word.

# 3. Sentiment Analysis in MATLAB

### 3.1 Fundamentals of Sentiment Analysis

#### 3.1.1 Definition and Classification of Sentiment Polarity

Sentiment polarity refers to the degree of positivity or negativity expressed in the text. In sentiment analysis, sentiment polarity is typically classified into the following categories:

- **Positive:** Expresses positive emotions or attitudes, such as happiness, satisfaction, excitement.
- **Negative:** Expresses negative emotions or attitudes, such as sadness, anger, disappointment.
- **Neutral:** Does not express a clear emotion or attitude.

#### 3.1.2 Methods of Sentiment Analysis

The methods of sentiment analysis are mainly divided into two types:

- **Rule-based methods:** Use predefined rules and dictionaries to identify and classify sentiment.
- **Machine learning-based methods:** Use machine learning algorithms to learn sentiment patterns from training data.

### 3.2 Sentiment Analysis Toolbox in MATLAB

MATLAB provides a toolbox called **Text Analytics Toolbox** that includes functions and methods for sentiment analysis.

#### 3.2.1 Extraction and Representation of Sentiment Features

Sentiment features are characteristics that describe the sentiment polarity of text. The sentiment analysis toolbox in MATLAB provides the following methods to extract sentiment features:

- **Bag-of-Words Model (BoW):** Represents text as a collection of words, with the frequency of each word's occurrence as a feature.
- **TF-IDF (Term Frequency-Inverse Document Frequency):** Weights the bag-of-words model by considering the frequency of words in the text and their frequency in the entire corpus.
- **Sentiment Dictionary:** Uses predefined sentiment dictionaries to identify sentiment words in the text.

#### 3.2.2 Construction and Evaluation of Sentiment Classifiers

A sentiment classifier is a machine learning model used to classify text based on its sentiment polarity. The sentiment analysis toolbox in MATLAB provides the following methods to build and evaluate sentiment classifiers: