文本情感分析（Sentiment Analysis）是自然语言处理（NLP）方法中常见的应用，也是一个有趣的基本任务，尤其是以提炼文本情绪内容为目的的分类。它是对带有情感色彩的主观性文本进行分析、处理、归纳和推理的过程。
本文将介绍情感分析中的情感极性（倾向）分析。所谓情感极性分析，指的是对文本进行褒义、贬义、中性的判断。在大多应用场景下，只分为两类。例如对于“喜爱”和“厌恶”这两个词，就属于不同的情感倾向。
数据集的下载网址为：https://github.com/renjunxiang/Text-Classification/blob/master/TextClassification/data/data_single.csv ，该数据集一共有4310条评论数据，文本的情感分为两类：“正面”和“反面.
一.流程分析
1、创建train reader 和 test_reader
2、创建lstm模型
3、定义 words、label 张量
4、优化函数、损失函数
5、训练 & 保存模型
6、使用模型进行预测
二、源代码
# coding=utf-8
import os
from multiprocessing import cpu_count
import numpy as np import paddle import paddle.fluid as fluidclass classify():
data_root_path = "" dict_path = "data/data9045/dict.txt" model_save_dir = "work/model_baseline/" test_data_path = "data/data9045/Test_IDs.txt" save_path = 'work/result.txt' # 获取字典长度 def get_dict_len(d_path): with open(d_path, 'r', encoding='utf-8') as f: line = eval(f.readlines()[0]) return len(line.keys()) # 1、创建train reader 和 test_reader def data_mapper(sample): data, label = sample data = [int(data) for data in data.split(',')] return data, int(label) # 创建数据读取器train_reader def train_reader(train_data_path): def reader(): with open(train_data_path, 'r') as f: lines = f.readlines() np.random.shuffle(lines) for line in lines: # print (line) data, label = line.split('\t') yield data, label return paddle.reader.xmap_readers(classify.data_mapper, reader, cpu_count(), 1024) # 创建数据读取器val_reader def val_reader(val_data_path): def reader(): with open(val_data_path, 'r') as f: lines = f.readlines() np.random.shuffle(lines) for line in lines: data, label = line.split('\t') yield data, label return paddle.reader.xmap_readers(classify.data_mapper, reader, cpu_count(), 1024) def test_reader(test_data_path): def reader(): with open(test_data_path, 'r') as f: lines = f.readlines() # 打乱 np.random.shuffle(lines) for line in lines: data = line yield data.strip(), -1 # 创建lstm网络 def lstm_net(data, dict_dim, class_dim=14, emb_dim=128, hid_dim=128, hid_dim2=96, ): """ Lstm net """ # embedding layer emb = fluid.layers.embedding( input=data, size=[dict_dim, emb_dim]) fc0 = fluid.layers.fc(input=emb, size=hid_dim * 4) lstm_h, c = fluid.layers.dynamic_lstm( input=fc0, size=hid_dim * 4, is_reverse=False) # extract last layer lstm_last = fluid.layers.sequence_last_step(input=lstm_h) # full connect layer fc1 = fluid.layers.fc(input=lstm_last, size=hid_dim2, act='tanh') # softmax layer prediction = fluid.layers.fc(input=fc1, size=class_dim, act='softmax') return prediction def train(self): # 获取训练数据读取器和测试数据读取器 train_reader = paddle.batch(reader=self.train_reader(os.path.join(self.data_root_path, "data/data9045/shuffle_Train_IDs.txt")), batch_size=128) val_reader = paddle.batch(reader=self.val_reader(os.path.join(self.data_root_path, "data/data9045/Val_IDs.txt")), batch_size=128) # 定义输入数据， lod_level不为0指定输入数据为序列数据 words = fluid.layers.data(name='words', shape=[1], dtype='int64', lod_level=1) label = fluid.layers.data(name='label', shape=[1], dtype='int64') dict_dim = self.get_dict_len(self.dict_path) # 获取分类器 model = self.lstm_net(words, dict_dim) # 获取损失函数和准确率 cost = fluid.layers.cross_entropy(input=model, label=label) avg_cost = fluid.layers.mean(cost) acc = fluid.layers.accuracy(input=model, label=label) # 获取预测程序 val_program = fluid.default_main_program().clone(for_test=True) # 定义优化方法 optimizer = fluid.optimizer.AdagradOptimizer(learning_rate=0.0001) opt = optimizer.minimize(avg_cost) # 创建一个执行器，CPU训练速度比较慢,此处选择gpu还是cpu #place = fluid.CPUPlace() place = fluid.CUDAPlace(0) exe = fluid.Executor(place) # 进行参数初始化 exe.run(fluid.default_startup_program()) # 定义数据映射器 feeder = fluid.DataFeeder(place=place, feed_list=[words, label]) EPOCH_NUM = 1 # 开始训练 for pass_id in range(EPOCH_NUM): # 进行训练 for batch_id, data in enumerate(train_reader()): # print(batch_id,len(data)) train_cost, train_acc = exe.run(program=fluid.default_main_program(), feed=feeder.feed(data), fetch_list=[avg_cost, acc]) if batch_id % 100 == 0: print('Pass:%d, Batch:%d, Cost:%0.5f, Acc:%0.5f' % (pass_id, batch_id, train_cost[0], train_acc[0])) # 进行测试 val_costs = [] val_accs = [] for batch_id, data in enumerate(val_reader()): val_cost, val_acc = exe.run(program=val_program, feed=feeder.feed(data), fetch_list=[avg_cost, acc]) val_costs.append(val_cost[0]) val_accs.append(val_acc[0]) # 计算每个epoch平均预测损失在和准确率 val_cost = (sum(val_costs) / len(val_costs)) val_acc = (sum(val_accs) / len(val_accs)) print('Test:%d, Cost:%0.5f, ACC:%0.5f' % (pass_id, val_cost, val_acc)) # 保存预测模型 if not os.path.exists(self.model_save_dir): os.makedirs(self.model_save_dir) fluid.io.save_inference_model(self.model_save_dir, feeded_var_names=[words.name], target_vars=[model], executor=exe) print('训练模型保存完成！') self.test(self) print('测试输出已生成！') # 获取数据 def get_data(self, sentence): # 读取数据字典 with open(self.dict_path, 'r', encoding='utf-8') as f_data: dict_txt = eval(f_data.readlines()[0]) dict_txt = dict(dict_txt) # 把字符串数据转换成列表数据 keys = dict_txt.keys() data = [] for s in sentence: # 判断是否存在未知字符 if not s in keys: s = '<unk>' data.append(int(dict_txt[s])) return data def test(self): data = [] # 获取预测数据 with open(self.test_data_path, 'r', encoding='utf-8') as test_data: lines = test_data.readlines() print('test start') for line in lines: tmp_sents = [] for word in line.strip().split(','): tmp_sents.append(int(word)) data.append(tmp_sents) ''' a=self.get_data(self, 'w我是共产主义接班人！') data=[a] ''' print(len(data)) def load_tensor(data): # 获取每句话的单词数量 base_shape = [[len(c) for c in data]] # 创建一个执行器，CPU训练速度比较慢 #place = fluid.CPUPlace() #GPU place = fluid.CUDAPlace(0) print('loading tensor') # 生成预测数据 tensor_words = fluid.create_lod_tensor(data, base_shape, place) #infer_place = fluid.CPUPlace() infer_place = fluid.CUDAPlace(0) # 执行预测 infer_exe = fluid.Executor(infer_place) # 进行参数初始化 infer_exe.run(fluid.default_startup_program()) # 从模型中获取预测程序、输入数据名称列表、分类器 print('load_model') [infer_program, feeded_var_names, target_var] = fluid.io.load_inference_model(dirname=self.model_save_dir, executor=infer_exe) print('getting_ans') result = infer_exe.run(program=infer_program, feed={feeded_var_names[0]: tensor_words}, fetch_list=target_var) names = ["财经", "彩票", "房产", "股票", "家居", "教育", "科技", "社会", "时尚", "时政", "体育", "星座", "游戏", "娱乐"] print('output') # 输出结果 for i in range(len(data)): lab = np.argsort(result)[0][i][-1] # print('预测结果标签为：%d， 名称为：%s， 概率为：%f' % (lab, names[lab], result[0][i][lab])) with open(self.save_path, 'a', encoding='utf-8') as ans: ans.write(names[lab] + "\n") ans.close() print('loading 1/4 data') load_tensor(data[:int(len(data)/4)]) print('loading 2/4 data') load_tensor(data[int(len(data)/4):2*int(len(data)/