WEBVTT 00:02.010 --> 00:05.850 So let's continue our discussion on GRITOS sentiment analysis project. 00:06.690 --> 00:10.020 So I have to find you a Ford video consumer. 00:10.060 --> 00:13.890 Presumably consumers see great access token and access open secret. 00:13.950 --> 00:15.160 And we help all those. 00:15.580 --> 00:18.490 So let us fill up all those video. 00:19.230 --> 00:21.780 So EPA is responding to consumer key. 00:22.170 --> 00:24.680 Lekman Selective EPA key. 00:26.850 --> 00:33.900 Let me keep it in first video, but then we have a EPA consumers secret key talking. 00:34.020 --> 00:35.130 So that will be dismissed. 00:38.190 --> 00:41.670 We have access token and access open secret. 00:42.030 --> 00:42.480 All right. 00:42.720 --> 00:43.560 Let me execute it. 00:43.890 --> 00:47.880 And we have defined this for video related to Dawkins'. 00:49.740 --> 00:57.720 Next is to get some data from this to decide what Delp of this Tolkan Velho Glass tablas for authenticate 00:57.810 --> 01:02.970 of a cell that we are the genuine use it based on whatever app we created. 01:03.000 --> 01:05.230 This NLB sentiment one to. 01:06.430 --> 01:09.270 So for that, we are going to use this gloopy library. 01:10.650 --> 01:12.880 Be not on hand, let. 01:14.210 --> 01:20.030 And just pass on consumer to key and consumer secret. 01:21.480 --> 01:22.290 Consumer secret. 01:24.100 --> 01:26.210 Let me assign you to some OK? 01:28.910 --> 01:35.290 And now inside this art object, we are going to set this access token. 01:36.890 --> 01:47.240 So that will be a set and the school access and a school Pocan access, token and access token secret. 01:47.630 --> 01:55.040 Now, once the heart of did God created, we can create the EPA, our coffee, so to speak, no EPA. 01:55.950 --> 01:59.510 And we can here to pass on this pork object for an indication. 02:00.090 --> 02:02.480 And it will be done as one EPA object. 02:03.050 --> 02:07.570 Now, from Vaizey gay object, we can affect everything from the solar. 02:10.270 --> 02:13.690 If you try to display what is the type of this EPA object? 02:15.640 --> 02:18.940 You can see it's a type of goopy, dark EPA, not EPA. 02:20.050 --> 02:24.010 One star turned Dickies and is successful with these two, don't celebrate. 02:24.980 --> 02:27.830 Now let's see how we can get some. 02:29.130 --> 02:30.390 It's based on some. 02:30.510 --> 02:31.110 He was. 02:32.320 --> 02:36.220 So we are going to define here are two very well, let's see. 02:36.370 --> 02:39.000 One is search term. 02:43.450 --> 02:48.830 Let's it so it's done will be our money, Heyst. 02:52.110 --> 02:55.260 And how many tweets you want to reply? 02:56.440 --> 02:57.880 So that will be a number of. 03:00.710 --> 03:01.080 It's. 03:02.130 --> 03:03.160 Let's see, thousand. 03:03.380 --> 03:04.380 Let me want to cry. 03:04.950 --> 03:07.410 Now let's use this therapy API. 03:08.140 --> 03:14.200 So repeat not we are going to use this concept in here. 03:14.280 --> 03:21.630 I can posit for which particular API you want to make this call predict will be API dart search. 03:23.430 --> 03:25.290 What which particular word? 03:26.990 --> 03:32.240 So that will be a cue equal to whatever search done we define. 03:35.310 --> 03:38.580 And how many tweets you want to read, right so far? 03:39.030 --> 03:41.010 You can use the items matter. 03:41.850 --> 03:44.580 And that will be a number of tweets. 03:46.140 --> 03:48.870 Let me assign it to do its. 03:50.820 --> 03:52.200 Let's just execute it. 03:53.200 --> 03:57.020 And if you just tried to bring this to. 04:00.570 --> 04:03.030 You can see we got the one Étretat object. 04:03.750 --> 04:06.210 That means we can do what, every single tweet. 04:06.480 --> 04:08.140 And get the facts out of it. 04:08.890 --> 04:15.540 So in the next video, we will see how to redo it every single day and find the sentiment associated 04:15.540 --> 04:15.900 with that. 04:16.800 --> 04:17.960 See you in the next video.