Steam游戏评论数据集，预测评论者是否推荐游戏

Steam 游戏评论数据集 预测评论者是否推荐游戏 电子游戏为娱乐业的发展做出了巨大贡献，并将继续做出贡献。1972年，当第一款电子游戏 "乒乓 "在街机机上推出时，它点燃了电子游戏的热潮，迅速席卷了年轻人。由此，雅达利游戏公司和任天堂等企业看到了投资发展中的娱乐行业的黄金机会，开始大肆生产游戏软件和硬件。这导致了视频游戏行业的崛起，自50年前成立以来，该行业已经创造了超过1090亿美元的收入和22亿的游戏玩家。 在这个拥有超过4700万日活跃用户的行业中，Steam已经运营了近16年。它的不断改进以更好地适应用户，使其发展在视频游戏行业中引人注目。 Steam是一个为游戏玩家和游戏开发者量身定做的数字发行平台。虽然它最初是为PC游戏服务的，但该平台很快就扩大到家用视频游戏机，如Xbox和索尼PlayStation。在Steam中，游戏玩家可以登录网站，方便地在线购买和玩耍游戏，这是购买游戏实体拷贝和手动下载到电脑上的更好选择。 很多玩家在游戏页面写评论，并可以选择是否向他人推荐这个游戏。然而，从文本中自动确定这种情绪，可以帮助Steam自动标记从互联网其他论坛中提取的这种评论，可以帮助他们更好地判断游戏的受欢迎程度。train和test的游戏概述信息都可以在train.zip里面的game_overview.csv单个文件中找到。 Steam digital distribution.

Steam Game Review Dataset: Predicting Whether Reviewers Recommend Games

Video games have made and will continue to make significant contributions to the development of the entertainment industry. In 1972, when the first video game *Pong* was launched on arcade machines, it ignited a craze for video games that quickly swept across young people. As a result, companies such as Atari and Nintendo recognized the golden opportunity to invest in this growing entertainment industry and began to ramp up production of game software and hardware. This led to the rise of the video game industry, which has generated over $109 billion in revenue and attracted 2.2 billion gamers since its inception 50 years ago.

Against the backdrop of this industry with over 47 million daily active users, Steam has been operating for nearly 16 years. Its continuous improvements to better adapt to users have made its development stand out in the video game industry.

Steam is a digital distribution platform custom-built for gamers and game developers. Although it initially served PC games, the platform quickly expanded to home video game consoles such as Xbox and Sony PlayStation. On Steam, gamers can log into the platform to conveniently purchase and play games online, which is a superior alternative to buying physical game copies and manually downloading them to computers.

A large number of players write reviews on game pages and can choose whether to recommend the game to others. However, automatically identifying this sentiment from the text can help Steam automatically tag such reviews extracted from other internet forums, enabling them to better judge the popularity of games. Game overview information for both the training and test sets can be found in the single file game_overview.csv contained within train.zip.

Steam digital distribution.

搜集汇总

背景与挑战

背景概述

该数据集是一个用于预测Steam游戏评论者是否推荐游戏的文本分类数据集，包含玩家评论和对应的推荐标签，旨在通过自然语言处理技术自动分析评论情绪，以辅助评估游戏受欢迎程度。数据集大小为7.4M，提供训练和测试集，并附带游戏概述信息，适用于游戏推荐和情感分析任务。