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Sentiment Analysis on Movie Reviews(BERT)

提交结果练习地址:https:www.kaggle.comcsentiment-analysis-on-movie-reviews 相关博文: SpamHam Email Classification

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spark杂记:movie recommendation using ALS

If no match found, return None Parameters ---------- fav_movie: str, name of user input movie Return , name of user input movie n_recommendations: int, top n recommendations Return ------ list of top n similar movie recommendations # create a userId userId = self.ratingsDF.agg({userId: max}).collect() recommendations Parameters ---------- fav_movie: str, name of user input movie n_recommendations: int , type=int, default=10, help=top n movie recommendations) return parser.parse_args() if __name__ == _

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    Text classification with TensorFlow Hub: Movie reviews

    This notebook classifies movie reviews as positive or negative using the text of the review. transfer learning with TensorFlow Hub and Keras.It uses the IMDB dataset that contains the text of 50,000 movie reviews from the Internet Movie Database. Each example is a sentence representing the movie review and a corresponding label.

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    图形数据库neo4j——(3)movie演示

    演员的饰演关系ACTED_IN 其中包括角色名称属性,导演关系DIRECTED 制片关系PRODUCED 编剧 WROTE

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    电影票APP原型设计分享– Movie Booking

    今天我用Mockplus做了一套5miles App的原型,这是5miles是一个基于地理位置的购物社区,用户通过通过5miles可以轻松、有趣、免费的进行买、...

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    cornell movie-dialogs corpus 康奈尔大学电影对话语料介绍及下载 可用于dialog,chatbot

    所以仔细阅读了readme,并记录相关要点所有文件以 +++$+++ 分隔符- movie_titles_metadata.txt - 包含每部电影标题信息 - fields: - movieID, - movie title, - movie year, - IMDB rating, - no. title - fields: - movieID, - movie title, - movie year, - IMDB rating, - no. character - fields: - characterID - character name - movieID - movie title - gender (? Movie Database; data interfaces available at http:www.imdb.cominterfaces).

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    Python爬虫——电影top榜

    = MaoyanItem() movie = item.xpath(.itext()).extract_first() movie = item.xpath(.divpatext()).extract_first () movie = item.xpath(.aimg@data-src).extract_first() movie = item.xpath(normalize-space(.divptext()) () movie = item.xpath(.divspantext()).extract_first() movie = item.xpath(.pspantext()).extract_first( (.*), content) if location: movie = .join(location.group(1).split()) movie = .join(location.group(2). split()) elif separator: movie = separator.group(1).strip() movie = separator.group(2).strip() movie

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    Python 101:如何从RottenTomatoes爬取数据

    in js : print rated: %s % movie print movie synopsis: + movie print critics_consensus: + movie print Major cast: for actor in movie : print %s as %s % ( actor , actor ) ratings = movie print cursor . execute ( sql , ( movie , movie , movie , movie , movie , movie , movie ) ) movie_id in js : print rated: %s % movie print movie synopsis: + movie print critics_consensus: + movie cursor . execute ( sql , ( movie , movie , movie , movie , movie , movie , movie ) ) movie_id

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    Key-Value Coding(KVC),Key-Value Observing(KVO)和Cocoa Bindings for MonoMac

    比如说你有个对象叫做Movie,有三个属性:Title,Producer,Year。 using System;using System.Collections.Generic; namespace KVC{ public partial class Movie { public Movie 的属性访问到:Movie movie = new Movie();movie.Title = Shrek - Forever After; to assign the valuevar title = movie = new Movie();movie.SetValueForKey((NSString)Shrek - Forever After,(NSString)Title);; to assign ,这样Movie就可以满足Cocoa的KVC机制。

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    21天打造分布式爬虫-数据解析实战(三)

    ,所有这里加个异常处理 try: screenshot = imgs #电影截图 movie = screenshot except IndexError: pass infos = zoomE.xpath (◎豆瓣评分, ).strip() movie = info elif info.startswith(◎片  长): info = info.replace(◎片  长, ).strip() movie = profile #下载地址 download_url = html.xpath(tda@href) # print(download_url) movie = download_url return movie def spider(): base_url = http:dytt8.nethtmlgndydyzzlist_23_{}.html movies = [] for x in range( = parse_detail_page(detail_url) movies.append(movie) print(movie) print(movies) #所有的电影信息 if __name__

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    《Pandas Cookbook》第01章 Pandas基础

    =object)# 给新列赋值 In: movie = (movie + movie + movie + movie) In: movie.isnull().sum()Out: 122# 用all()检查是否所有的布尔值都为 True In: movie = movie.fillna(0) In: movie = (movie >= movie) In: movie.all()Out: False In: movie = movie.drop (actor_director_facebook_likes, axis=columns) In: movie = (movie + movie + movie) movie = movie.fillna (0) In: movie = movie >= movie movie.all()Out: True In: movie = (movie movie) In: movie.min(), movie.max - movie) In: movie.head()Out:

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    爬取豆瓣电影排行top250

    , re.S) movies = re.findall(pattern, html) movie_list = , movie, movie.lstrip(  ), movie, movie.lstrip (), movie, movie.strip(), movie, movie, movie]) return movie_list def write_to_file(movie_list): with open(top_250.txt, w, encoding=utf-8,) as f: for movie in movie_list: f.write(电影排名: + movie + n) f.write (电影名称: + movie + n) f.write(电影别名: + movie + n) f.write(导演: + movie + n) f.write(上映年份: + movie + n) f.write (制作国家地区: + movie + n) f.write(电影类别: + movie + n) f.write(评分: + movie + n) f.write(参评人数: + movie + n)

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    微信小程序实战–集阅读与电影于一体的小程序项目(四)

    西虹市首富 movie-template.wxss@import ..starsstars-template.wxss; @import ..starsstars-template.wxss; .movie-container { display: flex; flex-direction: column; padding: 0 22rpx;} .movie-img { width: 200rpx; height: 270rpx ; padding-bottom: 20rpx;} .movie-title { margin-bottom: 16rpx; font-size: 24rpx;}movie-list-template.wxml 正在热映 更多 movie-list-template.wxss@import ..moviemovie-template.wxss; @import ..moviemovie-template.wxss { margin: 0 auto 20rpx;} .movie-head { padding: 30rpx 20rpx 22rpx;} .slogan { font-size: 24rpx;} .more

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    python3 类排序 类比较

    = *3 movie = Movies(电影1, 8.1) # movie_2 = Movies()movie = Movies(电影2, 9.2) movie = Movies(电影3, 3.4) for each in movie: print(each)print(-----n) sorted_movie = sorted(movie) for each in sorted_movie: print (each) print(movie > movie)输出结果 ? = *3 movie = Movies(电影1, 8.1) # movie_2 = Movies()movie = Movies(电影2, 9.2) movie = Movies(电影3, 3.4) # sorted_movie = sorted(movie, key=functools.cmp_to_key(cmp))sorted_movie = sorted(movie, key=lambda

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    爬虫实践--豆瓣电影当前上映电影信息爬取

    director = li.xpath(@data-director) actors = li.xpath(@data-actors) thumbnail = li.xpath(.img@src) movie score, 上映时间:release, 片长:duration, 制片国家:region, 导演:director, 演员表:actors, 海报:thumbnail } movies.append(movie (电影名: + movie + n) movie_file.write(评分: + movie + n) movie_file.write(上映时间: + movie + n) movie_file.write (片长: + movie + n) movie_file.write(制片国家: + movie + n) movie_file.write(导演: + movie + n) movie_file.write (演员表: + movie + n) movie_file.write(海报: + movie + n) movie_file.write(n)结果?

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    Swift 类型转换

    如果当前 MediaItem 是 Movie 类型的实例, item is Movie 返回 true ,反之返回 false 。 Movie 的结果是 Movie? 类型,也就是“可选 Movie 类型”。当数组中的 Song 实例使用向下转换至 Movie 类型时会失败。 Movie ”,它可以被读作:尝试以 Movie 类型访问 item 。如果成功,设置一个新的临时常量 movie 储存返回的可选 Movie 类型 。 如果向下类型转换成功, movie 的属性将用于输出 Movie 实例的描述信息,包括 director 的名字。 as Movie: print(a movie called (movie.name), dir.

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    requests+lxml+xpath爬取电影天堂

    =v elif v.startswith(◎产  地): v=parse_info(v,◎产  地) movie=v elif v.startswith(◎类  别): v=parse_info(v,◎ 类  别) movie=v elif v.startswith(◎豆瓣评分): v=parse_info(v,◎豆瓣评分).split() movie=v elif v.startswith(◎导  演 (k+1,len(infos)): actor=infos.strip() if actor.startswith(◎): break actors.append(actor) movie=actors =profile down_url=html.xpath(tda@href) movie=down_url return movie最后将这两个整合进一个爬虫中:def spider(): domain_url = parse_detail_page(detail_url) movies.append(movie) print(movies)运行爬虫,得到以下结果(在Json查看器中进行格式化):?

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    Python 实战(5):拿来主义

    in movie250: print movie, movie打印出结果,发现只有 20 条。 (movie) print movie, movie time.sleep(3)print movie_ids一切顺利,拿到 250 个 id,接下来就可以进行第二步,获取影片的详细信息了。 ): movie = json.loads(data) print movie db.insert(movie, id=int(movie), title=movie, origin=movie, url =movie, image=movie, directors=,.join( for d in movie]), casts=,.join( for c in movie]), year=movie, genres=,.join(movie), countries=,.join(movie), summary=movie, )之后,就是开始让程序反复地去请求、转换、存储。

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    Spring Boot JPA的查询语句

    title; private String director; private String rating; private int duration;} 构建初始化data.sql:INSERT INTO movie id, title, director, rating, duration) VALUES(3, Captain Marvel, Anna Boden, PG-13, 123);INSERT INTO movie (id, title, director, rating, duration) VALUES(4, Dumbo, Tim Burton, PG, 112);INSERT INTO movie(id, title , director, rating, duration) VALUES(5, Booksmart, Olivia Wilde, R, 102);INSERT INTO movie(id, title, extends JpaRepository {} Containing, Contains, IsContaining 和 Like如果我们想要构建模下面的模糊查询语句:SELECT * FROM movie

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    Python 实战(3):更多的页面

    同之前创建页面类似,首先在 urls 里增加一条跳转:urls = ( , index, movie(d+), movie,)d+ 是正则表达式,表示一个数字,之前的基础课程中有提到过(微信中回复 55 加上了括号,是为了让这个匹配到的数字可以被程序获取,成为后面所指向的 movie 中对应方法的参数。 新增处理请求的类 movie:class movie: def GET(self, movie_id): movie_id = int(movie_id) movie = db.select(movie 当在浏览器中访问诸如 movie123 的地址时,请求被转到 movie 中的 GET 方法,而 123 就成为参数 movie_id。 Movie Site 影片列表:$for movie in movies: $movie 在这个 标签中,movie 的 title 作为链接文字,id 拼接成跳转的地址。

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