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1. Base paper Title: Identifying Hot Topic Trends in Streaming Text Data Using News
Sequential Evolution Model Based on Distributed Representations
Modified Title: Finding Popular Topic Trends in Text Data Streaming by Utilising a News
Sequential Evolution Model with Distributed Representations
Abstract
Hot topic trends have become increasingly important in the era of social media, as these
trends can spread rapidly through online platforms and significantly impact public discourse
and behavior. As a result, the scope of distributed representations has expanded in machine
learning and natural language processing. As these approaches can be used to effectively
identify and analyze hot topic trends in large datasets. However, previous research has shown
that analyzing sequential periods in data streams to detect hot topic trends can be challenging,
particularly when dealing with large datasets. Moreover, existing methods often fail to
accurately capture the semantic relationships between words over different time periods,
limiting their effectiveness in trend prediction and relationship analysis. This paper aims to
utilize a distributed representations approach to detect hot topic trends in streaming text data.
For this purpose, we build a sequential evolution model for a streaming news website to
identify hot topic trends in streaming text data. Additionally, we create a visual display model
and knowledge graph to further enhance our proposed approach. To achieve this, we begin by
collecting streaming news data from the web and dividing it chronologically into several
datasets. In addition, word2vec models are built in different periods for each dataset. Finally,
we compare the relationship of any target word in sequential word2vec models and analyze its
evolutionary process. Experimental results show that the proposed method can detect hot topic
trends and provide a graphical representation of any raw data that cannot be easily designed
using traditional methods.
Existing System
Detecting hot topic trends in real-time is critical in many fields, including marketing,
technology, finance, and politics. However, traditional approaches to trend analysis often fall
short when it comes to understanding complex and nuanced language use in a continuous
stream of data. This is where distributed representation models, such as word2vec come in.
Word2Vec allows grouping similar words together and implementing learning algorithms to

2. improve performance on natural language processing tasks [1]. The model has attracted much
attention due to its ability to construct the semantic context of words [2], [3]. It contains many
algorithms and functions and can be implemented in Java, C, and Python. In short, word2vec
is a tool used for computing the vector representation of words. It inputs value as text and gives
output as word vectors. Although the usage of distributed representation models for creating
embeddings is widespread, many unanswered questions remain about the factors that influence
its results and its true capabilities [4], [5]. These models can efficiently capture the semantic
and syntactic relationships between words and phrases, allowing for more accurate and precise
trend analysis. In particular, the use of distributed representation models in a distributed
computing environment can enable real-time processing of massive amounts of data, making
it possible to detect and respond to emerging trends faster than ever before. Therefore,
developing and applying distributed representation models for trend analysis is an area of
growing importance and interest. Some of the current issues in hot topic trend detection include
the difficulty in handling large amounts of data, as well as the challenge of detecting subtle
shifts in language use and topic evolution over different time spans. Different areas of
application such as bioinformatics, data mining, speech recognition, remote sensing,
multimedia, text detection, localization, and others, require different techniques to be utilized.
Drawback in Existing System
 Semantic Ambiguity:
Drawback: Distributed representations often capture semantic information but may
struggle with resolving ambiguity. Words with multiple meanings or context-dependent
interpretations may pose challenges.
 Dynamic Nature of Language:
Drawback: Language is dynamic, and word meanings can change over time.
Distributed representations might not capture evolving semantic shifts effectively,
especially in the context of rapidly changing trends.

3.  Data Sparsity:
Drawback: In streaming text data, certain topics or events may be rare or occur
infrequently. This can result in sparse representations, making it challenging for models
to accurately capture and generalize from limited instances.
 Computational Resources:
Drawback: Training models for distributed representations often requires significant
computational resources. In a streaming environment, real-time processing can be
resource-intensive, and maintaining up-to-date models might be challenging.
Proposed System
 Data Collection:
Gather streaming text data from news articles, social media, or other relevant sources.
Ensure a continuous stream of data to capture real-time trends.
 Distributed Representations:
Utilize distributed representations (e.g., word embeddings like Word2Vec, GloVe, or
contextual embeddings like BERT) to encode the semantic meaning of words and
phrases in the text.
Train or use pre-trained embeddings on a large corpus to capture rich semantic
relationships.
 Temporal Evolution Model:
Design a model that captures the sequential evolution of news topics over time.
Consider recurrent neural networks (RNNs), long short-term memory networks
(LSTMs), or other sequential models to understand the temporal dynamics of topics.
 Scalability and Efficiency:
Ensure the system is scalable to handle large volumes of streaming data efficiently.
Optimize processing speed to maintain real-time capabilities.

4. Algorithm
 Word Embeddings:
Algorithm: Word2Vec, GloVe (Global Vectors for Word Representation), FastText.
Description: These algorithms generate distributed representations of words in a
continuous vector space, capturing semantic relationships between words. Each word
is represented as a dense vector, and similar words are close to each other in the vector
space.
 Document Embeddings:
Algorithm: Doc2Vec, paragraph embeddings.
Description: Extend the concept of word embeddings to entire documents. Each
document is represented as a vector in a continuous space, allowing for the comparison
and analysis of entire text bodies.
 Clustering Algorithms:
Algorithm: K-means, DBSCAN (Density-Based Spatial Clustering of Applications
with Noise), hierarchical clustering.
Description: Clustering algorithms can group similar documents or sentences together
based on their distributed representations. These clusters may represent different topics,
and their evolution over time can indicate emerging trends.
Advantages
 Semantic Understanding:
Advantage: Distributed representations capture semantic relationships between words
and phrases, allowing the model to understand the context and meaning of textual data.
This enhances the system's ability to identify and track emerging trends with a more
nuanced understanding of language.
 Real-time Adaptability:
Advantage: Streaming text data requires real-time adaptability. Models based on
distributed representations can be designed for online learning, allowing them to

5. continuously update and adapt as new data streams in. This ensures that the system
remains current and responsive to changing trends.
 Generalization:
Advantage: Models trained on distributed representations often generalize well to
different domains and datasets. This adaptability allows the system to perform
effectively across various types of streaming text data, making it versatile for different
applications.
 Interpretability:
Advantage: While interpretability can be a challenge in complex models, distributed
representations often capture meaningful semantic relationships. This can aid in
understanding why certain topics are related and how they evolve, providing valuable
insights for end-users.
Software Specification
 Processor : I3 core processor
 Ram : 4 GB
 Hard disk : 500 GB
Software Specification
 Operating System : Windows 10 /11
 Frond End : Python
 Back End : Mysql Server
 IDE Tools : Pycharm