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Neural networks (NNs) are computational models inspired by the structure and function of the human brain, designed to learn complex patterns and make predictions from data. They consist of interconnected nodes or neurons organized in layers, where each neuron processes input data and passes its output to the next layer. NNs are capable of learning from examples, adjusting their internal parameters through a process called training to improve their performance on specific tasks.
Figure 1 - Basic structure of a Neural Network
Figure 1 above illustrates a schematic representation of a neural network. On the left side, we see an input layer with five nodes. These nodes represent the features or input variables of the model. Each node in the input layer corresponds to a specific input feature
In the middle, there are two hidden layers. These layers are essential for learning complex patterns from the input data. The connections between nodes in these layers are fully connected, meaning each node is connected to every node in the adjacent layers. Hidden layers allow the neural network to learn hierarchical representations of the data.
On the right side, we find the output layer. It consists of a single node. The output layer produces the final prediction or classification result based on the learned features from the hidden layers. The lines connecting the nodes represent the connections (synapses) between neurons. These connections allow information to flow from one layer to another.
Each connection has an associated weight, which determines the strength of the signal passing through it. During training, the neural network adjusts these weights to minimize the difference between predicted outputs and actual targets (a process called back propagation).
Activation functions are applied to the output of each node in the hidden layers. They introduce non-linearity into the model.
Artificial Neural Networks (ANNs) are the foundation of neural network architectures, consisting of multiple layers of neurons interconnected by weighted edges. ANNs process data through these layers, with each neuron applying a mathematical operation to its inputs and passing the result through an activation function. They are versatile models used for various tasks, including classification, regression, and pattern recognition.
Perceptrons are the simplest form of neural networks, comprising a single layer of input nodes connected directly to an output node. They were among the earliest neural network models introduced and served as the building blocks for more complex architectures. Perceptrons learn to classify input data into two categories by adjusting the weights associated with each input.
Convolutional Neural Networks (CNNs) are specialized neural networks designed for processing structured grid-like data, such as images. CNNs leverage convolution operations to extract features from input data hierarchically, allowing them to learn spatial hierarchies of features. They are widely used in tasks like image recognition, object detection, and image segmentation due to their ability to capture local patterns efficiently.
Recurrent Neural Networks (RNNs) are neural networks designed to handle sequential data, where the order of elements matters. RNNs have connections that form directed cycles, allowing them to exhibit temporal dynamics and retain information over time. They are used in tasks like natural language processing, speech recognition, and time series analysis.
Long Short-Term Memory (LSTM) networks are a special type of RNN designed to address the vanishing gradient problem, which occurs when training deep neural networks on long sequences of data. LSTMs introduce a memory cell with self-loop connections that regulate the flow of information through the network. This architecture enables LSTMs to capture long-range dependencies and learn from sequences with long time lags more effectively.
Figure 2 - AI / ML / Deep Learning Hierarchy
Deep Learning is a subset of Machine Learning, which in turn is a subset of Artificial Intelligence. Within this hierarchy, Deep Learning occupies a pivotal position, offering a sophisticated approach to solving complex problems by leveraging neural networks with multiple layers. While Machine Learning algorithms encompass various techniques for learning from data and improving performance over time, Deep Learning specifically focuses on employing deep neural networks to automatically learn hierarchical representations of data.
Data Preparation
Data Preparation
When it comes to neural networks, particularly in the context of supervised learning, the principles of data preparation, including the use of labeled data and the separation into training and testing sets, remain fundamental. Let's elaborate:
1. Labeled Data:
Neural networks, like other supervised learning models, require labeled data to learn the relationships between input features and output labels. Each data point in the dataset should be associated with a corresponding label or target value. For example, in image recognition tasks, each image would have a label indicating the object or category it represents, enabling the neural network to learn to classify images correctly.
2. Training Set:
The training set is crucial for training neural networks. It consists of a portion of the labeled data used to update the network's parameters (weights and biases) during the training process. The neural network learns from the input-output pairs in the training set, adjusting its parameters iteratively through techniques like back propagation and gradient descent to minimize the difference between predicted and actual outputs.
3. Testing Set:
The testing set is a separate portion of the labeled data used to evaluate the neural network's performance after training. It serves as an independent dataset to assess how well the trained network generalizes to new, unseen examples. By evaluating the network on data it hasn't encountered during training, we can gauge its ability to make accurate predictions in real-world scenarios and detect any signs of over fitting.
Figure 3 - Train data for Neural Network
Figure 4 - Test data for Neural Network
Why must the training and testing sets be disjoint in the context of neural networks?
1. Preventing Over fitting:
Disjoint training and testing sets help prevent over fitting in neural networks. Over fitting occurs when the network learns to memorize the training data instead of capturing general patterns. If the same data points appear in both the training and testing sets, the network may perform well on the testing set due to memorization rather than true generalization.
2. Evaluating Generalization:
The testing set should represent new, unseen data that the network hasn't encountered during training. Disjoint sets ensure that the testing set provides an unbiased evaluation of the network's ability to generalize beyond the training data. This allows us to assess the network's performance in real-world scenarios where it must make predictions on unfamiliar data.
3. Real-world Performance:
Disjoint training and testing sets mimic real-world scenarios more accurately, reflecting situations where the network must generalize to new examples. By evaluating the network on disjoint data, we obtain a more reliable estimate of its performance in practical applications, which is essential for deploying neural network models in real-world settings.
Figure 5 - Train label data for Neural Network
Figure 6 -Test label data for Neural Network
Neural networks excel when dealing with numeric data due to their innate capacity to learn complex patterns and relationships within the data. In neural networks, each data point is fed through a network of interconnected nodes, where the numerical values are transformed and processed through various layers. This architecture allows neural networks to capture intricate patterns and dependencies among the input features, making them particularly adept at tasks such as image recognition, speech recognition, and natural language processing.
Moreover, neural networks thrive on labeled data as they rely on supervised learning techniques to train the model. By providing labeled examples to the network during the training phase, neural networks can iteratively adjust their parameters to minimize the difference between predicted and actual outputs, thus refining their predictive capabilities.
One of the key strengths of neural networks lies in their ability to learn hierarchical representations of data. Through multiple layers of neurons, neural networks can progressively extract and learn increasingly abstract and complex features from the input data. This hierarchical feature learning enables neural networks to automatically discover meaningful representations of the data, leading to superior performance in various tasks.
Additionally, neural networks are highly flexible and can adapt to different types of data and problem domains. Whether dealing with structured numerical data or unstructured data such as images and text, neural networks can be tailored and optimized to suit the specific characteristics of the dataset, making them a versatile choice for a wide range of applications.
Code
Code
Results
Results
Sentiment Analysis Results
Upon training the neural network model, it produced a commendable accuracy of 89% (Figure 7) on the training dataset, indicating its proficiency in capturing sentiment patterns within the text data. However, when the model was evaluated on the test dataset, the results were intriguing. The confusion matrix revealed that the predictions were 100% accurate (Figure 8). While this might initially suggest a high level of model performance, it also raises concerns about potential overfitting. Overfitting occurs when the model learns to memorize the training data rather than generalize patterns, thus performing exceptionally well on seen data but poorly on unseen data.
Figure 7 - Neural Network results
Figure 8 - Confusion Matrix
Sentiment Distribution
Figure 9- Sentiment Distribution
The analysis of sentiment towards climate change comments revealed an overall positive sentiment. Figure 9 shows the same, indicating that the majority of comments express optimistic or supportive sentiments regarding climate change-related topics. This positive sentiment trend is encouraging and aligns with the growing awareness and advocacy for climate action within communities.
Topic Modeling Results
Transitioning to topic modeling, the trained neural network model successfully identified key themes within the dataset, uncovering insights into the underlying topics of discussion. Specifically, the model extracted five distinct topics, each characterized by a set of significant keywords. These topics provide valuable insights into the prevalent themes discussed within the comments, enabling us to gain a deeper understanding of the diverse perspectives and concerns surrounding climate change.
Topic modeling serves as a powerful tool for uncovering latent themes and patterns within textual data. By analyzing the keywords associated with each topic, we can discern prominent themes and discussions prevalent within the dataset. This information is invaluable for stakeholders seeking to understand public sentiment, identify areas of concern, and tailor communication strategies effectively.
Figure 10 - Key words for each Topic
Dominant Topic Analysis
Upon analyzing the distribution of keywords across topics, Topic 2 emerged as the most prominent theme based on the frequency and significance of its associated keywords (Figure 10). This finding underscores the importance of exploring the nuances and complexities of this particular topic, as it appears to be a focal point of discussion within the dataset.
Figure 11 - Key words count for each topic.
Conclusion
Conclusion
Through the use of neural network technology, valuable insights were gained, into public sentiment regarding climate change. The analysis revealed an encouraging trend of positivity and support within the comments sampled, reflecting a growing awareness and concern for environmental issues among the general population.
These findings have significant implications for climate change advocacy and policy-making. The overwhelmingly positive sentiment suggests a fertile ground for fostering collective action and promoting sustainable initiatives aimed at mitigating and adapting to climate change challenges.
While the neural network demonstrated impressive accuracy in predicting sentiment, particularly on the test data, the possibility of overfitting poses a challenge. Beyond sentiment analysis, the neural network successfully identified key topics of discussion related to climate change. These topics provide valuable insights into the diverse perspectives and concerns within the community, offering stakeholders a nuanced understanding of the issues at hand.
Stakeholders, including policymakers, environmental activists, and community leaders, can leverage these insights to develop tailored engagement and communication strategies. By addressing specific topics and themes identified by the neural network, stakeholders can foster meaningful dialogue and encourage public participation in climate action initiatives.
Lastly, it's crucial to uphold ethical standards and ensure transparency in the use of AI technologies for climate change analysis. Respecting data privacy, mitigating biases, and communicating findings responsibly are essential steps in building trust and fostering inclusive, evidence-based decision-making processes.
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