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Sentiment analysis of movie reviews based on deep learning

Fuqian Zhang 1 , Qingtao Zeng 1 , Likun Lu 1 and Yeli Li 1

Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series , Volume 1754 , 2020 3rd International Symposium on Power Electronics and Control Engineering (ISPECE 2020) 27-29 November 2020, Chongqing, China Citation Fuqian Zhang et al 2021 J. Phys.: Conf. Ser. 1754 012234 DOI 10.1088/1742-6596/1754/1/012234

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1 School of Information Engineering, Beijing Institute of Graphic Communication, Bei Jing, 100000, China

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In recent years, with the rapid development of NLP (Natural Language Processing) and deep learning, public opinion and public opinion on the Internet have decreased a lot compared to the past. Many Internet users have changed from mere "bystanders" to disseminators of Internet information. Movie review sentiment analysis technology is an emerging category in the field of information mining. More and more people have joined the "review army". The quality of a movie is closely related to movie reviews! The manual screening method not only consumes a lot of manpower and material resources, but also is inefficient. Therefore, the use of deep learning-based sentiment analysis has become the current general trend. Based on the principle of word mosaic (word vector) and deep learning, this paper proposes a movie review sentiment analysis technology based on deep learning and machine learning word mosaic. Experiments show that the method used in this article has reached the correct rate of emotional classification of movie reviews. 83.13%, the experimental results prove the practicability and scalability of this method and the effectiveness of this method.

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Title: detecting spoilers in movie reviews with external movie knowledge and user networks.

Abstract: Online movie review platforms are providing crowdsourced feedback for the film industry and the general public, while spoiler reviews greatly compromise user experience. Although preliminary research efforts were made to automatically identify spoilers, they merely focus on the review content itself, while robust spoiler detection requires putting the review into the context of facts and knowledge regarding movies, user behavior on film review platforms, and more. In light of these challenges, we first curate a large-scale network-based spoiler detection dataset LCS and a comprehensive and up-to-date movie knowledge base UKM. We then propose MVSD, a novel Multi-View Spoiler Detection framework that takes into account the external knowledge about movies and user activities on movie review platforms. Specifically, MVSD constructs three interconnecting heterogeneous information networks to model diverse data sources and their multi-view attributes, while we design and employ a novel heterogeneous graph neural network architecture for spoiler detection as node-level classification. Extensive experiments demonstrate that MVSD advances the state-of-the-art on two spoiler detection datasets, while the introduction of external knowledge and user interactions help ground robust spoiler detection. Our data and code are available at this https URL

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International Congress on Information and Communication Technology

ICICT 2023: Proceedings of Eighth International Congress on Information and Communication Technology pp 421–434 Cite as

Sentiment Analysis of IMDB Movie Reviews Using Deep Learning Techniques

  • Beatriz Alejandra Bosques Palomo 13 ,
  • Flor Helena Valencia Velarde 13 ,
  • Francisco J. Cantu-Ortiz 13 &
  • Hector G. Ceballos Cancino 13  
  • Conference paper
  • First Online: 15 September 2023

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 696)

Movie reviews help users to evaluate and decide if a certain movie is of their particular interest. Nowadays, there is a lot of data about movies like IMDB which is an extensive database containing thousands of movie reviews. However, analyzing each of these reviews can be time consuming and tedious, so machine learning models could be implemented for automation and analysis of these reviews. Sentiment analysis is a process that uses artificial intelligence and machine learning to find a point of view, a keyword, or a feeling in order to highlight the information of interest in the process. In this sense, an opinion can be interpreted as a dimension in the data regarding a particular topic and can be very useful in various fields of application such as data mining, web mining, and social media analytics. This paper aims to use an IMDB database that contains 50,000 reviews, and we intend to apply transformer-based language models like Bidirectional Encoder Representations from Transformers (BERT), RoBERTa, and XLNet for sentiment analysis. Moreover, we implement a TF-IDF and cluster analysis to gain insights about the topics related to both positive and negative reviews (Yasser in IMDB movie ratings sentiment analysis, 2022 [ 1 ]; Kumar et al. in Int J Interact Multimed Artif Intell 5(5), 2019 [ 2 ]; Chakraborty et al. in Soc Netw Anal Comput Res Methods Tech 7:127–147, 2018 [ 3 ]; Gadekallu et al. in Sentiment analysis and knowledge discovery in contemporary business. IGI Global, pp 77–90, 2019 [ 4 ]).

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Yasser HM (2022) IMDB movie ratings sentiment analysis. Kaggle.com

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Kumar HM, Harish BS, Darshan HK (2019) Sentiment analysis on IMDb movie reviews using hybrid feature extraction method. Int J Interact Multimed Artif Intell 5(5). https://doi.org/10.9781/ijimai.2018.12.005

Chakraborty K, Bhattacharyya S, Bag R, Hassanien AA (2018) Sentiment analysis on a set of movie reviews using deep learning techniques. Soc Netw Anal Comput Res Methods Tech 7:127–147. https://doi.org/10.1016/B978-0-12-815458-8.00007-4

Gadekallu T, Soni A, Sarkar D, Kuruva L (2019) Application of sentiment analysis in movie reviews. In: Sentiment analysis and knowledge discovery in contemporary business. IGI Global, pp 77–90. https://doi.org/10.4018/978-1-5225-4999-4.ch006

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Pipalia K, Bhadja R, Shukla M (2020) Comparative analysis of different transformer based architectures used in sentiment analysis. In: 9th international conference system modeling and advancement in research trends (SMART). IEEE, pp 411–415. https://doi.org/10.1109/SMART50582.2020.9337081

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Zhang T, Xu B, Thung F, Haryono SA, Lo D, Jiang L (2020) Sentiment analysis for software engineering: how far can pre-trained transformer models go? In: 2020 IEEE international conference on software maintenance and evolution (ICSME). IEEE, pp 70–80. https://doi.org/10.1109/ICSME46990.2020.00017

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Darwis M, Pranoto GT, Wicaksana YE, Yaddarabullah Y (2020) Implementation of TF-IDF algorithm and K-mean clustering method to predict words or topics on Twitter. JISA (J Inf Sains) 3(2):49–55. https://doi.org/10.31326/jisa.v3i2.831

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Beatriz Alejandra Bosques Palomo, Flor Helena Valencia Velarde, Francisco J. Cantu-Ortiz & Hector G. Ceballos Cancino

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Palomo, B.A.B., Velarde, F.H.V., Cantu-Ortiz, F.J., Ceballos Cancino, H.G. (2024). Sentiment Analysis of IMDB Movie Reviews Using Deep Learning Techniques. In: Yang, XS., Sherratt, R.S., Dey, N., Joshi, A. (eds) Proceedings of Eighth International Congress on Information and Communication Technology. ICICT 2023. Lecture Notes in Networks and Systems, vol 696. Springer, Singapore. https://doi.org/10.1007/978-981-99-3236-8_33

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Everyday viewers now use social media platforms to express opinions on movies. Therefore if someone wants to watch a movie, he can now leverage thousands of reviews including ratings and comments on websites such as Rotten Tomatoes and IMDb. Comments are valuable if reviewer and reader have common focus in their viewing habits. In this paper, we examine whether users can intuit the focus of movie reviews in 6 dimensions: Human interest, Recommendations given, Extremity, Technicality, Comparativeness and Plot Summary. We find strong evidence to suggest that viewers can indeed connect with reviewers in some of these dimensions. We contribute to the literature by offering another way to approach movie review categorization by using Natural Language Processing (NLP).

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OpenAI teases an amazing new generative video model called Sora

The firm is sharing Sora with a small group of safety testers but the rest of us will have to wait to learn more.

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OpenAI has built a striking new generative video model called Sora that can take a short text description and turn it into a detailed, high-definition film clip up to a minute long.

Based on four sample videos that OpenAI shared with MIT Technology Review ahead of today’s announcement, the San Francisco–based firm has pushed the envelope of what’s possible with text-to-video generation (a hot new research direction that we flagged as a trend to watch in 2024 ).

“We think building models that can understand video, and understand all these very complex interactions of our world, is an important step for all future AI systems,” says Tim Brooks, a scientist at OpenAI.

But there’s a disclaimer. OpenAI gave us a preview of Sora (which means sky in Japanese) under conditions of strict secrecy. In an unusual move, the firm would only share information about Sora if we agreed to wait until after news of the model was made public to seek the opinions of outside experts. [Editor’s note: We’ve updated this story with outside comment below.] OpenAI has not yet released a technical report or demonstrated the model actually working. And it says it won’t be releasing Sora anytime soon. [ Update: OpenAI has now shared more technical details on its website.]

The first generative models that could produce video from snippets of text appeared in late 2022. But early examples from Meta , Google, and a startup called Runway were glitchy and grainy. Since then, the tech has been getting better fast. Runway’s gen-2 model, released last year, can produce short clips that come close to matching big-studio animation in their quality. But most of these examples are still only a few seconds long.  

The sample videos from OpenAI’s Sora are high-definition and full of detail. OpenAI also says it can generate videos up to a minute long. One video of a Tokyo street scene shows that Sora has learned how objects fit together in 3D: the camera swoops into the scene to follow a couple as they walk past a row of shops.

OpenAI also claims that Sora handles occlusion well. One problem with existing models is that they can fail to keep track of objects when they drop out of view. For example, if a truck passes in front of a street sign, the sign might not reappear afterward.  

In a video of a papercraft underwater scene, Sora has added what look like cuts between different pieces of footage, and the model has maintained a consistent style between them.

It’s not perfect. In the Tokyo video, cars to the left look smaller than the people walking beside them. They also pop in and out between the tree branches. “There’s definitely some work to be done in terms of long-term coherence,” says Brooks. “For example, if someone goes out of view for a long time, they won’t come back. The model kind of forgets that they were supposed to be there.”

Impressive as they are, the sample videos shown here were no doubt cherry-picked to show Sora at its best. Without more information, it is hard to know how representative they are of the model’s typical output.   

It may be some time before we find out. OpenAI’s announcement of Sora today is a tech tease, and the company says it has no current plans to release it to the public. Instead, OpenAI will today begin sharing the model with third-party safety testers for the first time.

In particular, the firm is worried about the potential misuses of fake but photorealistic video . “We’re being careful about deployment here and making sure we have all our bases covered before we put this in the hands of the general public,” says Aditya Ramesh, a scientist at OpenAI, who created the firm’s text-to-image model DALL-E .

But OpenAI is eyeing a product launch sometime in the future. As well as safety testers, the company is also sharing the model with a select group of video makers and artists to get feedback on how to make Sora as useful as possible to creative professionals. “The other goal is to show everyone what is on the horizon, to give a preview of what these models will be capable of,” says Ramesh.

To build Sora, the team adapted the tech behind DALL-E 3, the latest version of OpenAI’s flagship text-to-image model. Like most text-to-image models, DALL-E 3 uses what’s known as a diffusion model. These are trained to turn a fuzz of random pixels into a picture.

Sora takes this approach and applies it to videos rather than still images. But the researchers also added another technique to the mix. Unlike DALL-E or most other generative video models, Sora combines its diffusion model with a type of neural network called a transformer.

Transformers are great at processing long sequences of data, like words. That has made them the special sauce inside large language models like OpenAI’s GPT-4 and Google DeepMind’s Gemini . But videos are not made of words. Instead, the researchers had to find a way to cut videos into chunks that could be treated as if they were. The approach they came up with was to dice videos up across both space and time. “It’s like if you were to have a stack of all the video frames and you cut little cubes from it,” says Brooks.

The transformer inside Sora can then process these chunks of video data in much the same way that the transformer inside a large language model processes words in a block of text. The researchers say that this let them train Sora on many more types of video than other text-to-video models, varied in terms of resolution, duration, aspect ratio, and orientation. “It really helps the model,” says Brooks. “That is something that we’re not aware of any existing work on.”

“From a technical perspective it seems like a very significant leap forward,” says Sam Gregory, executive director at Witness, a human rights organization that specializes in the use and misuse of video technology. “But there are two sides to the coin,” he says. “The expressive capabilities offer the potential for many more people to be storytellers using video. And there are also real potential avenues for misuse.” 

OpenAI is well aware of the risks that come with a generative video model. We are already seeing the large-scale misuse of deepfake images . Photorealistic video takes this to another level.

Gregory notes that you could use technology like this to misinform people about conflict zones or protests. The range of styles is also interesting, he says. If you could generate shaky footage that looked like something shot with a phone, it would come across as more authentic.

The tech is not there yet, but generative video has gone from zero to Sora in just 18 months. “We’re going to be entering a universe where there will be fully synthetic content, human-generated content and a mix of the two,” says Gregory.

The OpenAI team plans to draw on the safety testing it did last year for DALL-E 3. Sora already includes a filter that runs on all prompts sent to the model that will block requests for violent, sexual, or hateful images, as well as images of known people. Another filter will look at frames of generated videos and block material that violates OpenAI’s safety policies.

OpenAI says it is also adapting a fake-image detector developed for DALL-E 3 to use with Sora. And the company will embed industry-standard C2PA tags , metadata that states how an image was generated, into all of Sora’s output. But these steps are far from foolproof. Fake-image detectors are hit-or-miss. Metadata is easy to remove, and most social media sites strip it from uploaded images by default.  

“We’ll definitely need to get more feedback and learn more about the types of risks that need to be addressed with video before it would make sense for us to release this,” says Ramesh.

Brooks agrees. “Part of the reason that we’re talking about this research now is so that we can start getting the input that we need to do the work necessary to figure out how it could be safely deployed,” he says.

Update 2/15: Comments from Sam Gregory were added .

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