978 research outputs found

    Comprehensive Review of Opinion Summarization

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    The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe

    Text summarization of online hotel reviews with sentiment analysis

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    The aim of this thesis is the creation of a system that summarizes positive and negative property reviews. To achieve this, an extractive summarization system that produces two summaries is proposed: one for the positive reviews and another for the negative ones. This is achieved with a classification system that will feed positive and nega- tive reviews to the summarization system. To pursue our objective, a study on the different NLP methods, along with their pros and cons, was performed, leading to the conclu- sion that the use of transformers and more specifically, the combination of BERT and GPT-2 architectures, would be the best approach. To obtain the data from TripAdvisor that is in StayForLong website, a crawling process was performed from the StayForLong and TripAdvi- sor. These consisted on a total of over 80000 reviews, and over 175 properties that we pre-processed, cleaned and tokenized, in order to work with BERT for the sentiment analysis and GPT-2 for the sum- marization. Then we proceeded, with an extensive analysis in regards to the impact of the variables. Finally, we fine-tuned each of the mod- els so that it performed at its possible best. To evaluate our two systems, we evaluated the the binary sen- timent classification system, with multi-modal BERT with a 96% of precision and for the GPT-2 summarization system, we opted to apply the ROUGE-F1 metric, were we obtained an average of 57.5%

    Online Crowds Opinion-Mining it to Analyze Current Trend: A Review

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    Online presence of the user has increased, there is a huge growth in the number of active users and thus the volume of data created on the online social networks is massive. Much are concentrating on the Internet Lingo. Notably most of the data on the social networking sites is made public which opens doors for companies, researchers and analyst to collect and analyze the data. We have huge volume of opinioned data available on the web we have to mine it so that we could get some interesting results out of it with could enhance the decision making process. In order to analyze the current scenario of what people are thinking focus is shifted towards opinion mining. This study presents a systematic literature review that contains a comprehensive overview of components of opinion mining, subjectivity of data, sources of opinion, the process and how does it let one analyze the current tendency of the online crowd in a particular context. Different perspectives from different authors regarding the above scenario have been presented. Research challenges and different applications that were developed with the motive opinion mining are also discussed

    AaKOS: Aspect-adaptive Knowledge-based Opinion Summarization

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    The rapid growth of information on the Internet has led to an overwhelming amount of opinions and comments on various activities, products, and services. This makes it difficult and time-consuming for users to process all the available information when making decisions. Text summarization, a Natural Language Processing (NLP) task, has been widely explored to help users quickly retrieve relevant information by generating short and salient content from long or multiple documents. Recent advances in pre-trained language models, such as ChatGPT, have demonstrated the potential of Large Language Models (LLMs) in text generation. However, LLMs require massive amounts of data and resources and are challenging to implement as offline applications. Furthermore, existing text summarization approaches often lack the ``adaptive" nature required to capture diverse aspects in opinion summarization, which is particularly detrimental to users with specific requirements or preferences. In this paper, we propose an Aspect-adaptive Knowledge-based Opinion Summarization model for product reviews, which effectively captures the adaptive nature required for opinion summarization. The model generates aspect-oriented summaries given a set of reviews for a particular product, efficiently providing users with useful information on specific aspects they are interested in, ensuring the generated summaries are more personalized and informative. Extensive experiments have been conducted using real-world datasets to evaluate the proposed model. The results demonstrate that our model outperforms state-of-the-art approaches and is adaptive and efficient in generating summaries that focus on particular aspects, enabling users to make well-informed decisions and catering to their diverse interests and preferences.Comment: 21 pages, 4 figures, 7 table
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